Date: (Mon) Jun 13, 2016

Introduction:

Data: Source: Training: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv
New: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv
Time period:

Synopsis:

Based on analysis utilizing <> techniques, :

Summary of key steps & error improvement stats:

Prediction Accuracy Enhancement Options:

  • transform.data chunk:
    • derive features from multiple features
  • manage.missing.data chunk:
    • Not fill missing vars
    • Fill missing numerics with a different algorithm
    • Fill missing chars with data based on clusters

[](.png)

Potential next steps include:

  • Organization:
    • Categorize by chunk
    • Priority criteria:
      1. Ease of change
      2. Impacts report
      3. Cleans innards
      4. Bug report
  • all chunks:
    • at chunk-end rm(!glb_)
  • manage.missing.data chunk:
    • cleaner way to manage re-splitting of training vs. new entity
  • extract.features chunk:
    • Add n-grams for glbFeatsText
      • “RTextTools”, “tau”, “RWeka”, and “textcat” packages
  • fit.models chunk:
    • Classification: Plot AUC Curves for all models & highlight glbMdlSel
    • Prediction accuracy scatter graph:
    • Add tiles (raw vs. PCA)
    • Use shiny for drop-down of “important” features
    • Use plot.ly for interactive plots ?

    • Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
    • create a custom model for rpart that has minbucket as a tuning parameter
    • varImp for randomForest crashes in caret version:6.0.41 -> submit bug report

  • Probability handling for multinomials vs. desired binomial outcome
  • ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
  • extensions toward multiclass classification are scheduled for the next release

  • fit.all.training chunk:
    • myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
  • Compare glb_sel_mdl vs. glb_fin_mdl:
    • varImp
    • Prediction differences (shd be minimal ?)
  • Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
  • Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
  • Parameterize glb_analytics_pn
  • Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
  • Do non-glm methods handle interaction terms ?
  • f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
  • Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
  • Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
  • Incorporate code chunks in print_sessionInfo
  • Test against
    • projects in github.com/bdanalytics
    • lectures in jhu-datascience track

Analysis:

rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
if (is.null(knitr::opts_current$get(name = 'label'))) # Running in IDE
    debugSource("~/Dropbox/datascience/R/mydsutils.R") else
    source("~/Dropbox/datascience/R/mydsutils.R")    
## Loading required package: caret
## Loading required package: lattice
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 10 # of cores on machine - 2
registerDoMC(glbCores) 

suppressPackageStartupMessages(require(caret))
require(plyr)
## Loading required package: plyr
require(dplyr)
## Loading required package: dplyr
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
require(knitr)
## Loading required package: knitr
require(stringr)
## Loading required package: stringr
#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")

# Analysis control global variables
# Inputs
#   url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>"; 
#               or named collection of <PathPointer>s
#   sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv"
    # or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
    #, splitSpecs = list(method = "copy" # default when glbObsNewFile is NULL
    #                       select from c("copy", NULL ???, "condition", "sample", )
    #                      ,nRatio = 0.3 # > 0 && < 1 if method == "sample" 
    #                      ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample" 
    #                      ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'    
    #                      )
    )                   
 
glbObsNewFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv") 

glbObsDropCondition <- #NULL # : default
#   enclose in single-quotes b/c condition might include double qoutes
#       use | & ; NOT || &&    
#   '<condition>' 
    # 'grepl("^First Draft Video:", glbObsAll$Headline)'
    # 'is.na(glbObsAll[, glb_rsp_var_raw])'
    # '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
    # 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
# '(is.na(glbObsAll[, "Q109244"]) | (glbObsAll[, "Q109244"] != "No"))' # No
'(glbObsAll[, "Q109244"] != "")' # NA
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
    
glb_obs_repartition_train_condition <- NULL # : default
#    "<condition>" 

glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
                         
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression; 
    glb_is_binomial <- TRUE # or TRUE or FALSE

glb_rsp_var_raw <- "Party"

# for classification, the response variable has to be a factor
glb_rsp_var <- "Party.fctr"

# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"), 
#   or contains spaces (e.g. "Not in Labor Force")
#   caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL 
function(raw) {
#     return(raw ^ 0.5)
#     return(log(raw))
#     return(log(1 + raw))
#     return(log10(raw)) 
#     return(exp(-raw / 2))
#     
# chk ref value against frequencies vs. alpha sort order
    ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == "Republican", "R", "D"); return(relevel(as.factor(ret_vals), ref = "D")) 
    
#     as.factor(paste0("B", raw))
#     as.factor(gsub(" ", "\\.", raw))
    }

#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw])))) 

#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
# print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))

glb_map_rsp_var_to_raw <- #NULL 
function(var) {
#     return(var ^ 2.0)
#     return(exp(var))
#     return(10 ^ var) 
#     return(-log(var) * 2)
#     as.numeric(var)
#     levels(var)[as.numeric(var)]
    sapply(levels(var)[as.numeric(var)], function(elm) 
        if (is.na(elm)) return(elm) else
        if (elm == 'R') return("Republican") else
        if (elm == 'D') return("Democrat") else
        stop("glb_map_rsp_var_to_raw: unexpected value: ", elm)
        )  
#     gsub("\\.", " ", levels(var)[as.numeric(var)])
#     c("<=50K", " >50K")[as.numeric(var)]
#     c(FALSE, TRUE)[as.numeric(var)]
}
# print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))

if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
    stop("glb_map_rsp_raw_to_var function expected")

# List info gathered for various columns
# <col_name>:   <description>; <notes>
# USER_ID - an anonymous id unique to a given user
# YOB - the year of birth of the user
# Gender - the gender of the user, either Male or Female
# Income - the household income of the user. Either not provided, or one of "under $25,000", "$25,001 - $50,000", "$50,000 - $74,999", "$75,000 - $100,000", "$100,001 - $150,000", or "over $150,000".
# HouseholdStatus - the household status of the user. Either not provided, or one of "Domestic Partners (no kids)", "Domestic Partners (w/kids)", "Married (no kids)", "Married (w/kids)", "Single (no kids)", or "Single (w/kids)".
# EducationalLevel - the education level of the user. Either not provided, or one of "Current K-12", "High School Diploma", "Current Undergraduate", "Associate's Degree", "Bachelor's Degree", "Master's Degree", or "Doctoral Degree".
# Party - the political party for whom the user intends to vote for. Either "Democrat" or "Republican
# Q124742, Q124122, . . . , Q96024 - 101 different questions that the users were asked on Show of Hands. If the user didn't answer the question, there is a blank. For information about the question text and possible answers, see the file Questions.pdf.

# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "USER_ID" # choose from c(NULL : default, "<id_feat>") 
glbFeatsCategory <- "Hhold.fctr" # choose from c(NULL : default, "<category_feat>")
# glbFeatsCategory <- "Q109244.fctr" # choose from c(NULL : default, "<category_feat>")
# glbFeatsCategory <- "Q115611.fctr" # choose from c(NULL : default, "<category_feat>")

# User-specified exclusions
glbFeatsExclude <- c(NULL
#   Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
#   Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
#   Feats that are linear combinations (alias in glm)
#   Feature-engineering phase -> start by excluding all features except id & category & 
#       work each one in
    , "USER_ID", "YOB", "Gender", "Income", "HouseholdStatus", "EducationLevel" 
    ,"Q124742","Q124122" 
    ,"Q123621","Q123464"
    ,"Q122771","Q122770","Q122769","Q122120"
    ,"Q121700","Q121699","Q121011"
    ,"Q120978","Q120650","Q120472","Q120379","Q120194","Q120014","Q120012" 
    ,"Q119851","Q119650","Q119334"
    ,"Q118892","Q118237","Q118233","Q118232","Q118117"
    ,"Q117193","Q117186"
    ,"Q116797","Q116881","Q116953","Q116601","Q116441","Q116448","Q116197"
    ,"Q115602","Q115777","Q115610","Q115611","Q115899","Q115390","Q115195"
    ,"Q114961","Q114748","Q114517","Q114386","Q114152"
    ,"Q113992","Q113583","Q113584","Q113181"
    ,"Q112478","Q112512","Q112270"
    ,"Q111848","Q111580","Q111220"
    ,"Q110740"
    ,"Q109367","Q109244"
    ,"Q108950","Q108855","Q108617","Q108856","Q108754","Q108342","Q108343"
    ,"Q107869","Q107491"
    ,"Q106993","Q106997","Q106272","Q106388","Q106389","Q106042"
    ,"Q105840","Q105655"
    ,"Q104996"
    ,"Q103293"
    ,"Q102906","Q102674","Q102687","Q102289","Q102089"
    ,"Q101162","Q101163","Q101596"
    ,"Q100689","Q100680","Q100562","Q100010"
    ,"Q99982"
    ,"Q99716"
    ,"Q99581"
    ,"Q99480"
    ,"Q98869"
    ,"Q98578"
    ,"Q98197"
    ,"Q98059","Q98078"
    ,"Q96024" # Done
    ,".pos") 
if (glb_rsp_var_raw != glb_rsp_var)
    glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)                    

glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"
glbFeatsInteractionOnly[["YOB.Age.dff"]] <- "YOB.Age.fctr"

glbFeatsDrop <- c(NULL
                # , "<feat1>", "<feat2>"
                )

glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"

# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();

# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
#     mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) } 
#   , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]

    # character
#     mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) } 
#     mapfn = function(Week) { return(substr(Week, 1, 10)) }
#     mapfn = function(Name) { return(sapply(Name, function(thsName) 
#                                             str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) } 

#     mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
#         "ABANDONED BUILDING"  = "OTHER",
#         "**"                  = "**"
#                                           ))) }

#     mapfn = function(description) { mod_raw <- description;
    # This is here because it does not work if it's in txt_map_filename
#         mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
    # Don't parse for "." because of ".com"; use customized gsub for that text
#         mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
    # Some state acrnoyms need context for separation e.g. 
    #   LA/L.A. could either be "Louisiana" or "LosAngeles"
        # modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
    #   OK/O.K. could either be "Oklahoma" or "Okay"
#         modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw); 
#         modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);        
    #   PR/P.R. could either be "PuertoRico" or "Public Relations"        
        # modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);        
    #   VA/V.A. could either be "Virginia" or "VeteransAdministration"        
        # modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
    #   
    # Custom mods

#         return(mod_raw) }

    # numeric
# Create feature based on record position/id in data   
glbFeatsDerive[[".pos"]] <- list(
    mapfn = function(raw1) { return(1:length(raw1)) }
    , args = c(".rnorm"))
# glbFeatsDerive[[".pos.y"]] <- list(
#     mapfn = function(raw1) { return(1:length(raw1)) }       
#     , args = c(".rnorm"))    

# Add logs of numerics that are not distributed normally
#   Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
#   Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
#     mapfn = function(WordCount) { return(log1p(WordCount)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
#     mapfn = function(WordCount) { return(WordCount ^ (1/2)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
#     mapfn = function(WordCount) { return(exp(-WordCount)) } 
#   , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
    
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
#     mapfn = function(District) {
#         raw <- District;
#         ret_vals <- rep_len("NA", length(raw)); 
#         ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm) 
#                                         ifelse(elm < 10, "1-9", 
#                                         ifelse(elm < 20, "10-19", "20+")));
#         return(relevel(as.factor(ret_vals), ref = "NA"))
#     }       
#     , args = c("District"))    

# YOB options:
# 1. Missing data:
# 1.1   0 -> Does not improve baseline
# 1.2   Cut factors & "NA" is a level
# 2. Data corrections: < 1928 & > 2000
# 3. Scale YOB
# 4. Add Age
# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.fctr"]] <- list(
    mapfn = function(raw1) {
        raw <- 2016 - raw1 
        # raw[!is.na(raw) & raw >= 2010] <- NA
        raw[!is.na(raw) & (raw <= 15)] <- NA
        raw[!is.na(raw) & (raw >= 90)] <- NA        
        retVal <- rep_len("NA", length(raw))
        # breaks = c(1879, seq(1949, 1989, 10), 2049)
        # cutVal <- cut(raw[!is.na(raw)], breaks = breaks, 
        #               labels = as.character(breaks + 1)[1:(length(breaks) - 1)])
        cutVal <- cut(raw[!is.na(raw)], breaks = c(15, 20, 25, 30, 35, 40, 50, 65, 90))
        retVal[!is.na(raw)] <- levels(cutVal)[cutVal]
        return(factor(retVal, levels = c("NA"
                ,"(15,20]","(20,25]","(25,30]","(30,35]","(35,40]","(40,50]","(50,65]","(65,90]"),
                        ordered = TRUE))
    }
    , args = c("YOB"))

# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.dff"]] <- list(
    mapfn = function(raw1) {
        raw <- 2016 - raw1 
        raw[!is.na(raw) & (raw <= 15)] <- NA
        raw[!is.na(raw) & (raw >= 90)] <- NA        
        breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)

        # retVal <- rep_len(0, length(raw))
        stopifnot(sum(!is.na(raw) && (raw <= 15)) == 0)
        stopifnot(sum(!is.na(raw) && (raw >= 90)) == 0) 
        # msk <- !is.na(raw) && (raw > 15) && (raw <= 20); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 15
        # msk <- !is.na(raw) && (raw > 20) && (raw <= 25); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 20
        # msk <- !is.na(raw) && (raw > 25) && (raw <= 30); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 25
        # msk <- !is.na(raw) && (raw > 30) && (raw <= 35); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 30
        # msk <- !is.na(raw) && (raw > 35) && (raw <= 40); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 35
        # msk <- !is.na(raw) && (raw > 40) && (raw <= 50); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 40
        # msk <- !is.na(raw) && (raw > 50) && (raw <= 65); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 50
        # msk <- !is.na(raw) && (raw > 65) && (raw <= 90); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 65

        breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)        
        retVal <- sapply(raw, function(age) {
            if (is.na(age)) return(0) else
            if ((age > 15) && (age <= 20)) return(age - 15) else
            if ((age > 20) && (age <= 25)) return(age - 20) else
            if ((age > 25) && (age <= 30)) return(age - 25) else
            if ((age > 30) && (age <= 35)) return(age - 30) else
            if ((age > 35) && (age <= 40)) return(age - 35) else
            if ((age > 40) && (age <= 50)) return(age - 40) else
            if ((age > 50) && (age <= 65)) return(age - 50) else
            if ((age > 65) && (age <= 90)) return(age - 65)
        })
        
        return(retVal)
    }
    , args = c("YOB"))

glbFeatsDerive[["Gender.fctr"]] <- list(
    mapfn = function(raw1) {
        raw <- raw1
        raw[raw %in% ""] <- "N"
        raw <- gsub("Male"  , "M", raw, fixed = TRUE)
        raw <- gsub("Female", "F", raw, fixed = TRUE)        
        return(relevel(as.factor(raw), ref = "N"))
    }
    , args = c("Gender"))

glbFeatsDerive[["Income.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("under $25,000"      , "<25K"    , raw, fixed = TRUE)
        raw <- gsub("$25,001 - $50,000"  , "25-50K"  , raw, fixed = TRUE)
        raw <- gsub("$50,000 - $74,999"  , "50-75K"  , raw, fixed = TRUE)
        raw <- gsub("$75,000 - $100,000" , "75-100K" , raw, fixed = TRUE)        
        raw <- gsub("$100,001 - $150,000", "100-150K", raw, fixed = TRUE)
        raw <- gsub("over $150,000"      , ">150K"   , raw, fixed = TRUE)        
        return(factor(raw, levels = c("N","<25K","25-50K","50-75K","75-100K","100-150K",">150K"),
                      ordered = TRUE))
    }
    , args = c("Income"))

glbFeatsDerive[["Hhold.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("Domestic Partners (no kids)", "PKn", raw, fixed = TRUE)
        raw <- gsub("Domestic Partners (w/kids)" , "PKy", raw, fixed = TRUE)        
        raw <- gsub("Married (no kids)"          , "MKn", raw, fixed = TRUE)
        raw <- gsub("Married (w/kids)"           , "MKy", raw, fixed = TRUE)        
        raw <- gsub("Single (no kids)"           , "SKn", raw, fixed = TRUE)
        raw <- gsub("Single (w/kids)"            , "SKy", raw, fixed = TRUE)        
        return(relevel(as.factor(raw), ref = "N"))
    }
    , args = c("HouseholdStatus"))

glbFeatsDerive[["Edn.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("Current K-12"         , "K12", raw, fixed = TRUE)
        raw <- gsub("High School Diploma"  , "HSD", raw, fixed = TRUE)        
        raw <- gsub("Current Undergraduate", "CCg", raw, fixed = TRUE)
        raw <- gsub("Associate's Degree"   , "Ast", raw, fixed = TRUE)
        raw <- gsub("Bachelor's Degree"    , "Bcr", raw, fixed = TRUE)        
        raw <- gsub("Master's Degree"      , "Msr", raw, fixed = TRUE)
        raw <- gsub("Doctoral Degree"      , "PhD", raw, fixed = TRUE)        
        return(factor(raw, levels = c("N","K12","HSD","CCg","Ast","Bcr","Msr","PhD"),
                      ordered = TRUE))
    }
    , args = c("EducationLevel"))

# for (qsn in c("Q124742","Q124122"))
# for (qsn in grep("Q12(.{4})(?!\\.fctr)", names(glbObsTrn), value = TRUE, perl = TRUE))
for (qsn in grep("Q", glbFeatsExclude, fixed = TRUE, value = TRUE))    
    glbFeatsDerive[[paste0(qsn, ".fctr")]] <- list(
        mapfn = function(raw1) {
            raw1[raw1 %in% ""] <- "NA"
            rawVal <- unique(raw1)
            
            if (length(setdiff(rawVal, (expVal <- c("NA", "No", "Ys")))) == 0) {
                raw1 <- gsub("Yes", "Ys", raw1, fixed = TRUE)
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Me", "Circumstances")))) == 0) {
                raw1 <- gsub("Circumstances", "Cs", raw1, fixed = TRUE)
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Grrr people", "Yay people!")))) == 0) {
                raw1 <- gsub("Grrr people", "Gr", raw1, fixed = TRUE)
                raw1 <- gsub("Yay people!", "Yy", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Idealist", "Pragmatist")))) == 0) {
                raw1 <- gsub("Idealist"  , "Id", raw1, fixed = TRUE)
                raw1 <- gsub("Pragmatist", "Pr", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Private", "Public")))) == 0) {
                raw1 <- gsub("Private", "Pt", raw1, fixed = TRUE)
                raw1 <- gsub("Public" , "Pc", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            }
            
            return(relevel(as.factor(raw1), ref = "NA"))
        }
        , args = c(qsn))

# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
#     mapfn = function(FertilityRate, Region) {
#         RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
# 
#         retVal <- FertilityRate
#         retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
#         return(retVal)
#     }
#     , args = c("FertilityRate", "Region"))
    
#     mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }     
#     mapfn = function(Rasmussen)  { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) } 
#     mapfn = function(startprice) { return(startprice ^ (1/2)) }       
#     mapfn = function(startprice) { return(log(startprice)) }   
#     mapfn = function(startprice) { return(exp(-startprice / 20)) }
#     mapfn = function(startprice) { return(scale(log(startprice))) }     
#     mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }        

    # factor      
#     mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
#     mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
#     mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
#     mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5)); 
#                             tfr_raw[is.na(tfr_raw)] <- "NA.my";
#                             return(as.factor(tfr_raw)) }
#     mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
#     mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }    

#     , args = c("<arg1>"))
    
    # multiple args
#     mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }        
#     mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
#     mapfn = function(startprice.log10.predict, startprice) {
#                  return(spdiff <- (10 ^ startprice.log10.predict) - startprice) } 
#     mapfn = function(productline, description) { as.factor(
#         paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
#     mapfn = function(.src, .pos) { 
#         return(paste(.src, sprintf("%04d", 
#                                    ifelse(.src == "Train", .pos, .pos - 7049)
#                                    ), sep = "#")) }       

# # If glbObsAll is not sorted in the desired manner
#     mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }

# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]

# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst))); 
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]); 

glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <- 
#     c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE, 
#       last.ctg = FALSE, poly.ctg = FALSE)

glbFeatsPrice <- NULL # or c("<price_var>")

glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation

glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english
## [1] "C/C/C/C/C/en_US.UTF-8"
#glbFeatsText[["<TextFeature>"]] <- list(NULL,
#   ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-screened-names>
#   ))))
#   ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-nonSCOWL-words>
#   ))))
#)

# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"

# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
    require(tm)
    require(stringr)

    glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
        # Remove any words from stopwords            
#         , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
                                
        # Remove salutations
        ,"mr","mrs","dr","Rev"                                

        # Remove misc
        #,"th" # Happy [[:digit::]]+th birthday 

        # Remove terms present in Trn only or New only; search for "Partition post-stem"
        #   ,<comma-separated-terms>        

        # cor.y.train == NA
#         ,unlist(strsplit(paste(c(NULL
#           ,"<comma-separated-terms>"
#         ), collapse=",")

        # freq == 1; keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>        
                                            )))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]

# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))

# To identify terms with a specific freq & 
#   are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")

#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]

# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))

# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)

# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])

# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")

# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]

# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Person names for names screening
#         ,<comma-separated-list>
#         
#         # Company names
#         ,<comma-separated-list>
#                     
#         # Product names
#         ,<comma-separated-list>
#     ))))

# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Words not in SCOWL db
#         ,<comma-separated-list>
#     ))))

# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)

# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
# 
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")

# To identify which stopped words are "close" to a txt term
#sort(glbFeatsCluster)

# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))

# Text Processing Step: mycombineSynonyms
#   To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
#   To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
#     cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
    print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
    print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
#     cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
#     cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl",  syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag",  syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent",  syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use",  syns=c("use", "usag")))

glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
#     # people in places
#     , list(word = "australia", syns = c("australia", "australian"))
#     , list(word = "italy", syns = c("italy", "Italian"))
#     , list(word = "newyork", syns = c("newyork", "newyorker"))    
#     , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))    
#     , list(word = "peru", syns = c("peru", "peruvian"))
#     , list(word = "qatar", syns = c("qatar", "qatari"))
#     , list(word = "scotland", syns = c("scotland", "scotish"))
#     , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))    
#     , list(word = "venezuela", syns = c("venezuela", "venezuelan"))    
# 
#     # companies - needs to be data dependent 
#     #   - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#         
#     # general synonyms
#     , list(word = "Create", syns = c("Create","Creator")) 
#     , list(word = "cute", syns = c("cute","cutest"))     
#     , list(word = "Disappear", syns = c("Disappear","Fadeout"))     
#     , list(word = "teach", syns = c("teach", "taught"))     
#     , list(word = "theater",  syns = c("theater", "theatre", "theatres")) 
#     , list(word = "understand",  syns = c("understand", "understood"))    
#     , list(word = "weak",  syns = c("weak", "weaken", "weaker", "weakest"))
#     , list(word = "wealth",  syns = c("wealth", "wealthi"))    
#     
#     # custom synonyms (phrases)
#     
#     # custom synonyms (names)
#                                       )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
#     , list(word="<stem1>",  syns=c("<stem1>", "<stem1_2>"))
#                                       )

for (txtFeat in names(glbFeatsTextSynonyms))
    for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)        
    }        

glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART 
glb_txt_terms_control <- list( # Gather model performance & run-time stats
                    # weighting = function(x) weightSMART(x, spec = "nnn")
                    # weighting = function(x) weightSMART(x, spec = "lnn")
                    # weighting = function(x) weightSMART(x, spec = "ann")
                    # weighting = function(x) weightSMART(x, spec = "bnn")
                    # weighting = function(x) weightSMART(x, spec = "Lnn")
                    # 
                    weighting = function(x) weightSMART(x, spec = "ltn") # default
                    # weighting = function(x) weightSMART(x, spec = "lpn")                    
                    # 
                    # weighting = function(x) weightSMART(x, spec = "ltc")                    
                    # 
                    # weighting = weightBin 
                    # weighting = weightTf 
                    # weighting = weightTfIdf # : default
                # termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
                    , bounds = list(global = c(1, Inf)) 
                # wordLengths selection criteria: tm default: c(3, Inf)
                    , wordLengths = c(1, Inf) 
                              ) 

glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)

# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq" 
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)

# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default 
names(glbFeatsTextAssocCor) <- names(glbFeatsText)

# Remember to use stemmed terms
glb_important_terms <- list()

# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")

# Have to set it even if it is not used
# Properties:
#   numrows(glb_feats_df) << numrows(glbObsFit
#   Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
#       numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)

glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer

glbFeatsCluster <- paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".") # NULL : glbFeatsCluster <- c("YOB.Age.fctr", "Gender.fctr", "Income.fctr", 
                     # # "Hhold.fctr",
                     # "Edn.fctr",
                     # paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".")) # NULL : default or c("<feat1>", "<feat2>")
# glbFeatsCluster <- grep(paste0("[", 
#                         toupper(paste0(substr(glbFeatsText, 1, 1), collapse = "")),
#                                       "]\\.[PT]\\."), 
#                                names(glbObsAll), value = TRUE)

glb_cluster.seed <- 189 # or any integer
glbClusterEntropyVar <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsClusterVarsExclude <- FALSE # default FALSE

glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")

glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default

glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
# glbRFESizes[["RFE.X"]] <- c(2, 3, 4, 5, 6, 7, 8, 16, 32, 64, 128, 247) # accuracy(5) = 0.6154
# glbRFESizes[["Final"]] <- c(8, 16, 32, 40, 44, 46, 48, 49, 50, 51, 52, 56, 64, 96, 128, 247) # accuracy(49) = 0.6164

glbRFEResults <- NULL

glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
#     is.na(.rstudent)
#     max(.rstudent)
#     is.na(.dffits)
#     .hatvalues >= 0.99        
#     -38,167,642 < minmax(.rstudent) < 49,649,823    
#     , <comma-separated-<glbFeatsId>>
#                                     )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
                                c(NULL
                                ))

# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()

# Add xgboost algorithm

# Regression
if (glb_is_regression) {
    glbMdlMethods <- c(NULL
        # deterministic
            #, "lm", # same as glm
            , "glm", "bayesglm", "glmnet"
            , "rpart"
        # non-deterministic
            , "gbm", "rf" 
        # Unknown
            , "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
            , "bagEarth" # Takes a long time
            ,"xgbLinear","xgbTree"
        )
} else
# Classification - Add ada (auto feature selection)
    if (glb_is_binomial)
        glbMdlMethods <- c(NULL
        # deterministic                     
            , "bagEarth" # Takes a long time        
            , "glm", "bayesglm", "glmnet"
            , "nnet"
            , "rpart"
        # non-deterministic        
            , "gbm"
            , "avNNet" # runs 25 models per cv sample for tunelength=5      
            , "rf"
        # Unknown
            , "lda", "lda2"
                # svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
            ,"xgbLinear","xgbTree"
        ) else
        glbMdlMethods <- c(NULL
        # deterministic
            ,"glmnet"
        # non-deterministic 
            ,"rf"       
        # Unknown
            ,"gbm","rpart","xgbLinear","xgbTree"
        )

glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "Csm.X", "All.X", "Best.Interact") %*% c(NUll, ".NOr", ".Inc")
#   RFE = "Recursive Feature Elimination"
#   Csm = CuStoM
#   NOr = No OutlieRs
#   Inc = INteraCt
#   methods: Choose from c(NULL, <method>, glbMdlMethods) 
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial) {
    # glm does not work for multinomial
    glbMdlFamilies[["All.X"]] <- c("glmnet") 
} else {
    # glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")
    glbMdlFamilies[["All.X"]] <- c("glmnet")
    # glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm")
    # glbMdlFamilies[["RFE.X"]] <- c("glmnet")    
    # glbMdlFamilies[["RFE.X"]] <- setdiff(glbMdlMethods, c(NULL
    #     # , "bayesglm" # error: Error in trControl$classProbs && any(classLevels != make.names(classLevels)) : invalid 'x' type in 'x && y'
    #     # , "lda","lda2" # error: Error in lda.default(x, grouping, ...) : variable 236 appears to be constant within groups
    #     , "svmLinear" # Error in .local(object, ...) : test vector does not match model ! In addition: Warning messages:
    #     , "svmLinear2" # SVM has not been trained using `probability = TRUE`, probabilities not available for predictions
    #     , "svmPoly" # runs 75 models per cv sample for tunelength=5 # took > 2 hrs # Error in .local(object, ...) : test vector does not match model !
    #     , "svmRadial" # Error in .local(object, ...) : test vector does not match model !
    #     ,"xgbLinear","xgbTree" # Need clang-omp compiler; Upgrade to Revolution R 3.2.3 (3.2.2 current); https://github.com/dmlc/xgboost/issues/276 thread
    #                                     ))
}
# glbMdlFamilies[["All.X.Inc"]] <- glbMdlFamilies[["All.X"]] # value not used
# glbMdlFamilies[["RFE.X.Inc"]] <- glbMdlFamilies[["RFE.X"]] # value not used

# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
#     , <comma-separated-features-vector>
#                                   )
# dAFeats.CSM.X %<d-% c(NULL
#     # Interaction feats up to varImp(RFE.X.glmnet) >= 50
#     , <comma-separated-features-vector>
#     , setdiff(myextract_actual_feats(predictors(glbRFEResults)), c(NULL
#                , <comma-separated-features-vector>
#                                                                       ))    
#                                   )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"

# glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")

glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["Final##rcv#glmnet"]] <- FALSE

# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()

# When glmnet crashes at model$grid with error: ???
# AllX__rcv_glmnetTuneParams <- rbind(data.frame()
#     ,data.frame(parameter = "alpha",  vals = "0.100 0.325 0.550 0.775 1.000")
#     ,data.frame(parameter = "lambda", vals = "0.05 0.06367626 0.07 0.08 0.09167068")
#                         ) # max.Accuracy.OOB = 0.6020202 @ 0.55 0.03

# glbMdlTuneParams <- rbind(glbMdlTuneParams
#     ,cbind(data.frame(mdlId = "All.X##rcv#glmnet"),            AllX__rcv_glmnetTuneParams)
# )

    #avNNet    
    #   size=[1] 3 5 7 9; decay=[0] 1e-04 0.001  0.01   0.1; bag=[FALSE]; RMSE=1.3300906 

    #bagEarth
    #   degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# bagEarthTuneParams <- rbind(data.frame()
#                         ,data.frame(parameter = "degree", vals = "1")
#                         ,data.frame(parameter = "nprune", vals = "256")
#                         )
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
#                                cbind(data.frame(mdlId = "Final.RFE.X.Inc##rcv#bagEarth"),
#                                      bagEarthTuneParams))

# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
#     ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")    
# ))

    #earth 
    #   degree=[1]; nprune=2  [9] 17 25 33; RMSE=0.1334478
    
    #gbm 
    #   shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313     
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
#     ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
#     ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
#     ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
#     #seq(from=0.05,  to=0.25, by=0.05)
# ))

    #glmnet
    #   alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
#     ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")    
# ))

    #nnet    
    #   size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
#     ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")    
# ))

    #rf # Don't bother; results are not deterministic
    #       mtry=2  35  68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))

    #rpart 
    #   cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()    
#     ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
    
    #svmLinear
    #   C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))

    #svmLinear2    
    #   cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354 
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))

    #svmPoly    
    #   degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
#     ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
#     ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")    
# ))

    #svmRadial
    #   sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
    
#glb2Sav(); all.equal(sav_models_df, glb_models_df)

pkgPreprocMethods <-     
# caret version: 6.0.068 # packageVersion("caret")
# operations are applied in this order: zero-variance filter, near-zero variance filter, Box-Cox/Yeo-Johnson/exponential transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign
# *Impute methods needed only if NAs are fed to myfit_mdl
#   Also, ordered.factor in caret creates features as Edn.fctr^4 which is treated as an exponent by bagImpute
    c(NULL
      ,"zv", "nzv"
      ,"BoxCox", "YeoJohnson", "expoTrans"
      ,"center", "scale", "center.scale", "range"
      ,"knnImpute", "bagImpute", "medianImpute"
      ,"zv.pca", "ica", "spatialSign"
      ,"conditionalX") 

glbMdlPreprocMethods <- list(NULL# NULL # : default
    # ,"All.X" = list("glmnet" = union(setdiff(pkgPreprocMethods,
    #                                         c("knnImpute", "bagImpute", "medianImpute")),
    #                                 # c(NULL)))
    #                                 c("zv.pca.spatialSign")))
    # ,"RFE.X" = list("glmnet" = union(setdiff(pkgPreprocMethods,
    #                                         c("knnImpute", "bagImpute", "medianImpute")),
    #                                 c(NULL)))
    #                                 # c("zv.pca.spatialSign")))
)
# glbMdlPreprocMethods[["RFE.X"]] <- list("glmnet" = union(unlist(glbMdlPreprocMethods[["All.X"]]),
#                                                     "nzv.pca.spatialSign"))

# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")

glbMdlMetric_terms <- NULL # or matrix(c(
#                               0,1,2,3,4,
#                               2,0,1,2,3,
#                               4,2,0,1,2,
#                               6,4,2,0,1,
#                               8,6,4,2,0
#                           ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression) 
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
#     confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
#     #print(confusion_mtrx)
#     #print(confusion_mtrx * glbMdlMetric_terms)
#     metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
#     names(metric) <- glbMdlMetricSummary
#     return(metric)
# }

glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL

glb_clf_proba_threshold <- NULL # 0.5

# Model selection criteria
if (glb_is_regression)
    glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "min.elapsedtime.everything",
                           "max.Adj.R.sq.fit", "min.RMSE.fit")
    #glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")    
if (glb_is_classification) {
    if (glb_is_binomial)
        glbMdlMetricsEval <- 
            c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB",
              "min.elapsedtime.everything", 
              # "min.aic.fit", 
              "max.Accuracy.fit") else        
        glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB", "min.elapsedtime.everything")
}

# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glbMdlEnsemble <- NULL # NULL : default #"auto"
#     "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')" 
#     c(<comma-separated-mdlIds>
#      )
glbMdlEnsembleSampleMethods <- c("boot", "boot632", "cv", "repeatedcv"
               # , "LOOCV" # tuneLength * nrow(fitDF) # way too many models
               , "LGOCV"
               , "adaptive_cv" # crashed for Q109244No
               # , "adaptive_boot"  #error: adaptive$min should be less than 3
               # , "adaptive_LGOCV" #error: adaptive$min should be less than 3
               )


# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)

glbMdlSelId <- NULL #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glbMdlFinId <- NULL #select from c(NULL, glbMdlSelId)

glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
#               List critical cols excl. above
                  )

# Output specs
# lclgetfltout_df <- function(obsOutFinDf) {
#     require(tidyr)
#     obsOutFinDf <- obsOutFinDf %>%
#         tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"), 
#                         sep = "#", remove = TRUE, extra = "merge")
#     # mnm prefix stands for max_n_mean
#     mnmout_df <- obsOutFinDf %>%
#         dplyr::group_by(.pos) %>%
#         #dplyr::top_n(1, Probability1) %>% # Score = 3.9426         
#         #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;         
#         #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169; 
#         dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;        
#         #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#     
#         # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))    
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
#         dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), 
#                          yMeanN = weighted.mean(as.numeric(y), c(Probability1)))  
#     
#     maxout_df <- obsOutFinDf %>%
#         dplyr::group_by(.pos) %>%
#         dplyr::summarize(maxProb1 = max(Probability1))
#     fltout_df <- merge(maxout_df, obsOutFinDf, 
#                        by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
#                        all.x = TRUE)
#     fmnout_df <- merge(fltout_df, mnmout_df, 
#                        by.x = c(".pos"), by.y = c(".pos"),
#                        all.x = TRUE)
#     return(fmnout_df)
# }
glbObsOut <- list(NULL
        # glbFeatsId will be the first output column, by default
        ,vars = list()
#         ,mapFn = function(obsOutFinDf) {
#                   }
                  )
#obsOutFinDf <- savobsOutFinDf
# glbObsOut$mapFn <- function(obsOutFinDf) {
#     txfout_df <- dplyr::select(obsOutFinDf, -.pos.y) %>%
#         dplyr::mutate(
#             lunch     = levels(glbObsTrn[, "lunch"    ])[
#                        round(mean(as.numeric(glbObsTrn[, "lunch"    ])), 0)],
#             dinner    = levels(glbObsTrn[, "dinner"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "dinner"   ])), 0)],
#             reserve   = levels(glbObsTrn[, "reserve"  ])[
#                        round(mean(as.numeric(glbObsTrn[, "reserve"  ])), 0)],
#             outdoor   = levels(glbObsTrn[, "outdoor"  ])[
#                        round(mean(as.numeric(glbObsTrn[, "outdoor"  ])), 0)],
#             expensive = levels(glbObsTrn[, "expensive"])[
#                        round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
#             liquor    = levels(glbObsTrn[, "liquor"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "liquor"   ])), 0)],
#             table     = levels(glbObsTrn[, "table"    ])[
#                        round(mean(as.numeric(glbObsTrn[, "table"    ])), 0)],
#             classy    = levels(glbObsTrn[, "classy"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "classy"   ])), 0)],
#             kids      = levels(glbObsTrn[, "kids"     ])[
#                        round(mean(as.numeric(glbObsTrn[, "kids"     ])), 0)]
#                       )
#     
#     print("ObsNew output class tables:")
#     print(sapply(c("lunch","dinner","reserve","outdoor",
#                    "expensive","liquor","table",
#                    "classy","kids"), 
#                  function(feat) table(txfout_df[, feat], useNA = "ifany")))
#     
#     txfout_df <- txfout_df %>%
#         dplyr::mutate(labels = "") %>%
#         dplyr::mutate(labels = 
#     ifelse(lunch     != "-1", paste(labels, lunch    ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(dinner    != "-1", paste(labels, dinner   ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(reserve   != "-1", paste(labels, reserve  ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(outdoor   != "-1", paste(labels, outdoor  ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(liquor    != "-1", paste(labels, liquor   ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(table     != "-1", paste(labels, table    ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(classy    != "-1", paste(labels, classy   ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(kids      != "-1", paste(labels, kids     ), labels)) %>%
#         dplyr::select(business_id, labels)
#     return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsOutFinDf <- glbObsOut$mapFn(obsOutFinDf); print(head(obsOutFinDf))

glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")

if (glb_is_classification && glb_is_binomial) {
    # glbObsOut$vars[["Probability1"]] <- 
    #     "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$prob]" 
    # glbObsOut$vars[[glb_rsp_var_raw]] <-
    #     "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
    #                                         mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
    glbObsOut$vars[["Predictions"]] <-
        "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
                                            mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
} else {
#     glbObsOut$vars[[glbFeatsId]] <- 
#         "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
    glbObsOut$vars[[glb_rsp_var]] <- 
        "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$value]"
#     for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
#         glbObsOut$vars[[outVar]] <- 
#             paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}    
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-

glbOutStackFnames <- # NULL #: default
    c("Q109244No_AllXpreProc_cnk03_rest_out_fin.csv") 
    # c("Votes_Ensemble_cnk06_out_fin.csv") 


glbOut <- list(pfx = "Q109244NA_AllX_cnk01_fit.models_1_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")


glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
    ,"import.data","inspect.data","scrub.data","transform.data"
    ,"extract.features"
        ,"extract.features.datetime","extract.features.image","extract.features.price"
        ,"extract.features.text","extract.features.string"  
        ,"extract.features.end"
    ,"manage.missing.data","cluster.data","partition.data.training","select.features"
    ,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
    ,"fit.data.training_0","fit.data.training_1"
    ,"predict.data.new"         
    ,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
    !identical(chkChunksLabels, glbChunks$labels)) {
    print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s", 
                  setdiff(chkChunksLabels, glbChunks$labels)))    
    print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s", 
                  setdiff(glbChunks$labels, chkChunksLabels)))    
}

glbChunks[["first"]] <- NULL # NULL # default: script will load envir from previous chunk
glbChunks[["last" ]] <- "fit.models_1" # default: script will save envir at end of this chunk 
glbChunks[["inpFilePathName"]] <- NULL #"data/Q109244No_AllXNOr_cnk01_fit.models_1_fit.models_1.RData" # NULL: default or "data/<prvScriptName>_<lstChunkLbl>.RData"
#mysavChunk(glbOut$pfx, glbChunks[["last"]]) # called from myevlChunk
# Temporary: Delete this function (if any) from here after appropriate .RData file is saved

# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])

#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))

# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
                        trans_df = data.frame(id = 1:6,
    name = c("data.training.all","data.new",
           "model.selected","model.final",
           "data.training.all.prediction","data.new.prediction"),
    x=c(   -5,-5,-15,-25,-25,-35),
    y=c(   -5, 5,  0,  0, -5,  5)
                        ),
                        places_df=data.frame(id=1:4,
    name=c("bgn","fit.data.training.all","predict.data.new","end"),
    x=c(   -0,   -20,                    -30,               -40),
    y=c(    0,     0,                      0,                 0),
    M0=c(   3,     0,                      0,                 0)
                        ),
                        arcs_df = data.frame(
    begin = c("bgn","bgn","bgn",        
            "data.training.all","model.selected","fit.data.training.all",
            "fit.data.training.all","model.final",    
            "data.new","predict.data.new",
            "data.training.all.prediction","data.new.prediction"),
    end   = c("data.training.all","data.new","model.selected",
            "fit.data.training.all","fit.data.training.all","model.final",
            "data.training.all.prediction","predict.data.new",
            "predict.data.new","data.new.prediction",
            "end","end")
                        ))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())
## Loading required package: grid

glb_analytics_avl_objs <- NULL

glb_chunks_df <- myadd_chunk(NULL, 
                             ifelse(is.null(glbChunks$first), "import.data", glbChunks$first))
##         label step_major step_minor label_minor   bgn end elapsed
## 1 import.data          1          0           0 7.843  NA      NA

Step 1.0: import data

chunk option: eval=

## [1] "Reading file ./data/train2016.csv..."
## [1] "dimensions of data in ./data/train2016.csv: 5,568 rows x 108 cols"
##   USER_ID  YOB Gender              Income            HouseholdStatus
## 1       1 1938   Male                               Married (w/kids)
## 2       4 1970 Female       over $150,000 Domestic Partners (w/kids)
## 3       5 1997   Male  $75,000 - $100,000           Single (no kids)
## 4       8 1983   Male $100,001 - $150,000           Married (w/kids)
## 5       9 1984 Female   $50,000 - $74,999           Married (w/kids)
## 6      10 1997 Female       over $150,000           Single (no kids)
##        EducationLevel      Party Q124742 Q124122 Q123464 Q123621 Q122769
## 1                       Democrat      No              No      No      No
## 2   Bachelor's Degree   Democrat             Yes      No      No      No
## 3 High School Diploma Republican             Yes     Yes      No        
## 4   Bachelor's Degree   Democrat      No     Yes      No     Yes      No
## 5 High School Diploma Republican      No     Yes      No      No      No
## 6        Current K-12   Democrat                                      No
##   Q122770 Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650
## 1     Yes  Public      No     Yes      No              No      No     Yes
## 2     Yes  Public      No     Yes      No     Yes      No      No     Yes
## 3     Yes Private      No      No      No     Yes      No      No     Yes
## 4      No  Public      No     Yes      No     Yes      No      No     Yes
## 5     Yes  Public      No     Yes      No     Yes     Yes      No     Yes
## 6     Yes  Public      No      No      No     Yes      No     Yes     Yes
##   Q120472     Q120194 Q120012 Q120014 Q119334 Q119851   Q119650 Q118892
## 1           Try first      No      No             Yes               Yes
## 2 Science Study first     Yes     Yes      No      No Receiving      No
## 3 Science Study first             Yes      No     Yes Receiving      No
## 4 Science   Try first      No     Yes     Yes      No    Giving     Yes
## 5     Art   Try first     Yes      No      No      No    Giving      No
## 6 Science   Try first     Yes     Yes      No     Yes Receiving      No
##   Q118117    Q118232 Q118233 Q118237     Q117186        Q117193 Q116797
## 1     Yes   Idealist      No      No                                Yes
## 2      No Pragmatist      No      No Cool headed Standard hours      No
## 3     Yes Pragmatist      No     Yes Cool headed      Odd hours      No
## 4      No   Idealist      No      No Cool headed Standard hours      No
## 5      No   Idealist     Yes     Yes  Hot headed Standard hours      No
## 6      No Pragmatist      No      No             Standard hours        
##   Q116881 Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777 Q115610
## 1   Happy     Yes     Yes      No      No    P.M.     Yes   Start     Yes
## 2   Happy     Yes     Yes     Yes      No    A.M.      No     End     Yes
## 3   Right     Yes      No      No     Yes    A.M.     Yes   Start     Yes
## 4   Happy     Yes     Yes      No      No    A.M.     Yes   Start     Yes
## 5   Happy     Yes     Yes      No     Yes    P.M.      No     End      No
## 6                                                                        
##   Q115611       Q115899 Q115390 Q114961 Q114748 Q115195 Q114517    Q114386
## 1      No Circumstances     Yes     Yes     Yes     Yes      No           
## 2      No            Me     Yes     Yes      No     Yes      No Mysterious
## 3     Yes Circumstances      No     Yes      No     Yes     Yes Mysterious
## 4      No Circumstances     Yes      No      No     Yes      No        TMI
## 5      No            Me      No     Yes     Yes     Yes     Yes        TMI
## 6                                                                         
##   Q113992 Q114152 Q113583    Q113584 Q113181 Q112478 Q112512 Q112270
## 1     Yes     Yes    Talk Technology      No      No     Yes        
## 2      No      No                                                   
## 3      No      No   Tunes Technology     Yes     Yes     Yes     Yes
## 4      No      No    Talk     People      No     Yes     Yes     Yes
## 5     Yes      No   Tunes     People      No      No     Yes      No
## 6                                                                   
##   Q111848    Q111580 Q111220 Q110740 Q109367       Q108950 Q109244 Q108855
## 1      No  Demanding      No              No      Cautious      No    Yes!
## 2                                Mac     Yes      Cautious      No  Umm...
## 3      No Supportive      No      PC      No      Cautious      No  Umm...
## 4     Yes Supportive      No     Mac     Yes Risk-friendly      No  Umm...
## 5      No  Demanding     Yes      PC     Yes      Cautious      No    Yes!
## 6     Yes Supportive      No      PC                                      
##   Q108617   Q108856 Q108754   Q108342 Q108343 Q107869 Q107491 Q106993
## 1      No     Space      No In-person             Yes      No     Yes
## 2      No     Space     Yes In-person      No     Yes     Yes      No
## 3      No     Space      No In-person      No      No     Yes     Yes
## 4      No Socialize     Yes    Online      No     Yes      No     Yes
## 5      No Socialize      No    Online      No      No     Yes     Yes
## 6                           In-person      No      No     Yes     Yes
##       Q106997 Q106272 Q106388 Q106389 Q106042 Q105840 Q105655 Q104996
## 1 Yay people!     Yes      No     Yes     Yes              No     Yes
## 2 Yay people!     Yes     Yes     Yes     Yes     Yes      No     Yes
## 3 Grrr people     Yes      No      No      No      No      No      No
## 4 Grrr people      No      No     Yes     Yes      No     Yes     Yes
## 5 Yay people!     Yes      No     Yes     Yes     Yes     Yes      No
## 6 Grrr people     Yes      No     Yes     Yes      No      No     Yes
##   Q103293 Q102906 Q102674 Q102687 Q102289 Q102089   Q101162 Q101163
## 1      No      No      No     Yes      No     Own  Optimist        
## 2                                                                  
## 3     Yes      No      No     Yes      No     Own Pessimist     Mom
## 4      No      No      No     Yes     Yes     Own  Optimist     Mom
## 5      No      No     Yes      No      No     Own  Optimist     Mom
## 6     Yes     Yes      No     Yes                                  
##   Q101596 Q100689 Q100680 Q100562 Q99982 Q100010 Q99716 Q99581 Q99480
## 1     Yes     Yes      No      No   Nope     Yes     No     No       
## 2                                                                  No
## 3      No      No      No      No   Nope     Yes     No     No     No
## 4      No      No      No     Yes Check!      No     No     No    Yes
## 5      No     Yes     Yes     Yes   Nope     Yes     No     No    Yes
## 6                                                                    
##   Q98869 Q98578     Q98059 Q98078 Q98197 Q96024
## 1     No        Only-child     No     No    Yes
## 2     No     No Only-child    Yes     No     No
## 3    Yes     No        Yes     No    Yes     No
## 4    Yes     No        Yes     No     No    Yes
## 5     No     No        Yes     No     No    Yes
## 6                                              
##      USER_ID  YOB Gender              Income             HouseholdStatus
## 193      245 1964   Male       over $150,000            Married (w/kids)
## 848     1046 1953   Male $100,001 - $150,000 Domestic Partners (no kids)
## 2836    3530 1995   Male                                Single (no kids)
## 4052    5050 1945 Female  $75,000 - $100,000            Married (w/kids)
## 4093    5107 1980 Female $100,001 - $150,000            Married (w/kids)
## 5509    6888 1998 Female       under $25,000            Single (no kids)
##             EducationLevel      Party Q124742 Q124122 Q123464 Q123621
## 193      Bachelor's Degree Republican     Yes     Yes      No     Yes
## 848                          Democrat                                
## 2836 Current Undergraduate   Democrat     Yes     Yes     Yes      No
## 4052     Bachelor's Degree Republican                                
## 4093     Bachelor's Degree   Democrat                      No      No
## 5509          Current K-12 Republican                                
##      Q122769 Q122770 Q122771 Q122120 Q121699 Q121700 Q120978 Q121011
## 193       No     Yes  Public      No     Yes      No     Yes      No
## 848                                                                 
## 2836             Yes  Public     Yes      No      No     Yes     Yes
## 4052              No  Public                                        
## 4093      No      No Private      No                                
## 5509                                                     Yes     Yes
##      Q120379 Q120650 Q120472     Q120194 Q120012 Q120014 Q119334 Q119851
## 193       No     Yes Science   Try first     Yes     Yes     Yes      No
## 848                                                                     
## 2836     Yes     Yes     Art Study first      No     Yes             Yes
## 4052                                                                    
## 4093                                                         Yes        
## 5509     Yes      No     Art Study first     Yes      No     Yes      No
##      Q119650 Q118892 Q118117    Q118232 Q118233 Q118237     Q117186
## 193   Giving     Yes      No   Idealist     Yes     Yes  Hot headed
## 848                                                                
## 2836             Yes     Yes   Idealist     Yes      No Cool headed
## 4052                      No                 No      No            
## 4093              No      No Pragmatist      No     Yes            
## 5509  Giving      No                                               
##             Q117193 Q116797 Q116881 Q116953 Q116601 Q116441 Q116448
## 193  Standard hours      No   Happy     Yes     Yes      No      No
## 848                                                                
## 2836      Odd hours      No   Happy     Yes     Yes              No
## 4052                                                               
## 4093                                                               
## 5509                                                               
##      Q116197 Q115602 Q115777 Q115610 Q115611       Q115899 Q115390 Q114961
## 193     A.M.     Yes     End     Yes     Yes            Me      No      No
## 848                                                                       
## 2836             Yes     End     Yes      No Circumstances     Yes      No
## 4052    P.M.     Yes   Start     Yes      No                    No        
## 4093    P.M.     Yes   Start     Yes      No Circumstances                
## 5509                                                                      
##      Q114748 Q115195 Q114517    Q114386 Q113992 Q114152 Q113583    Q113584
## 193      Yes      No     Yes        TMI      No     Yes   Tunes Technology
## 848                                                                       
## 2836     Yes      No      No Mysterious      No     Yes   Tunes     People
## 4052      No     Yes                                                      
## 4093                                                      Tunes     People
## 5509                                                                      
##      Q113181 Q112478 Q112512 Q112270 Q111848    Q111580 Q111220 Q110740
## 193       No     Yes             Yes     Yes Supportive      No     Mac
## 848                                                                    
## 2836     Yes     Yes     Yes      No     Yes  Demanding     Yes      PC
## 4052                                                                   
## 4093                                     Yes Supportive                
## 5509                                                                   
##      Q109367       Q108950 Q109244 Q108855 Q108617   Q108856 Q108754
## 193       No      Cautious      No    Yes!      No Socialize      No
## 848      Yes Risk-friendly     Yes    Yes!      No     Space      No
## 2836     Yes      Cautious     Yes             Yes                  
## 4052                                                                
## 4093      No Risk-friendly      No    Yes!      No     Space      No
## 5509                                                                
##        Q108342 Q108343 Q107869 Q107491 Q106993     Q106997 Q106272 Q106388
## 193  In-person      No     Yes     Yes      No Yay people!     Yes     Yes
## 848  In-person     Yes                                                    
## 2836 In-person     Yes             Yes                         Yes      No
## 4052                                        No Grrr people                
## 4093 In-person     Yes     Yes     Yes     Yes Yay people!     Yes     Yes
## 5509                                                                      
##      Q106389 Q106042 Q105840 Q105655 Q104996 Q103293 Q102906 Q102674
## 193       No     Yes      No      No     Yes      No      No      No
## 848                                                                 
## 2836     Yes      No      No      No     Yes     Yes      No      No
## 4052                              No      No      No              No
## 4093      No      No      No      No     Yes      No      No     Yes
## 5509                                                                
##      Q102687 Q102289 Q102089  Q101162 Q101163 Q101596 Q100689 Q100680
## 193       No      No     Own Optimist     Dad     Yes     Yes      No
## 848                                                                  
## 2836     Yes     Yes    Rent Optimist     Dad      No     Yes     Yes
## 4052     Yes             Own                       No                
## 4093     Yes     Yes    Rent                               No     Yes
## 5509                                                                 
##      Q100562 Q99982 Q100010 Q99716 Q99581 Q99480 Q98869 Q98578 Q98059
## 193      Yes Check!      No     No     No    Yes    Yes     No    Yes
## 848                                                                  
## 2836     Yes Check!      No     No     No    Yes    Yes           Yes
## 4052                                                                 
## 4093      No   Nope     Yes     No    Yes    Yes    Yes     No    Yes
## 5509                                                                 
##      Q98078 Q98197 Q96024
## 193      No    Yes    Yes
## 848                    No
## 2836    Yes    Yes     No
## 4052                     
## 4093    Yes    Yes     No
## 5509                     
##      USER_ID  YOB Gender            Income  HouseholdStatus
## 5563    6955 1966   Male     over $150,000 Married (w/kids)
## 5564    6956   NA   Male                                   
## 5565    6957 2000 Female                                   
## 5566    6958 1969   Male     over $150,000                 
## 5567    6959 1986   Male $25,001 - $50,000 Married (w/kids)
## 5568    6960 1999   Male     under $25,000 Single (no kids)
##           EducationLevel      Party Q124742 Q124122 Q123464 Q123621
## 5563   Bachelor's Degree   Democrat                                
## 5564     Master's Degree   Democrat              No      No        
## 5565        Current K-12 Republican                                
## 5566   Bachelor's Degree   Democrat                             Yes
## 5567 High School Diploma Republican                                
## 5568        Current K-12 Republican                                
##      Q122769 Q122770 Q122771 Q122120 Q121699 Q121700 Q120978 Q121011
## 5563                              No     Yes      No     Yes     Yes
## 5564      No     Yes  Public             Yes                        
## 5565                  Public                             Yes        
## 5566                              No      No      No     Yes     Yes
## 5567                             Yes             Yes              No
## 5568                                     Yes      No      No        
##      Q120379 Q120650 Q120472   Q120194 Q120012 Q120014 Q119334 Q119851
## 5563                                                                  
## 5564                                                                  
## 5565     Yes     Yes     Art Try first      No     Yes     Yes     Yes
## 5566     Yes     Yes Science                                          
## 5567      No      No Science                No     Yes                
## 5568                                                                  
##        Q119650 Q118892 Q118117 Q118232 Q118233 Q118237 Q117186 Q117193
## 5563                                                                  
## 5564                                                                  
## 5565 Receiving                                                        
## 5566                                                                  
## 5567                                                                  
## 5568                                                                  
##      Q116797 Q116881 Q116953 Q116601 Q116441 Q116448 Q116197 Q115602
## 5563                                                                
## 5564                                                                
## 5565                                                                
## 5566                                                                
## 5567                                                                
## 5568                                                                
##      Q115777 Q115610 Q115611 Q115899 Q115390 Q114961 Q114748 Q115195
## 5563                                                                
## 5564                                                                
## 5565                                                                
## 5566                                                                
## 5567                                                                
## 5568                                                                
##      Q114517 Q114386 Q113992 Q114152 Q113583 Q113584 Q113181 Q112478
## 5563                                                                
## 5564                                                                
## 5565                                                                
## 5566                                                                
## 5567                                                                
## 5568                                                                
##      Q112512 Q112270 Q111848 Q111580 Q111220 Q110740 Q109367 Q108950
## 5563                                                                
## 5564                                                                
## 5565                                                                
## 5566                                                                
## 5567                                                                
## 5568                                                                
##      Q109244 Q108855 Q108617 Q108856 Q108754 Q108342 Q108343 Q107869
## 5563                                                                
## 5564                                                                
## 5565                                                                
## 5566                                                                
## 5567                                                                
## 5568                                                                
##      Q107491 Q106993 Q106997 Q106272 Q106388 Q106389 Q106042 Q105840
## 5563                                                                
## 5564                                                                
## 5565                                                                
## 5566                                                                
## 5567                                                                
## 5568                                                                
##      Q105655 Q104996 Q103293 Q102906 Q102674 Q102687 Q102289 Q102089
## 5563                                                                
## 5564                                                                
## 5565                                                                
## 5566                                                                
## 5567                                                                
## 5568                                                                
##      Q101162 Q101163 Q101596 Q100689 Q100680 Q100562 Q99982 Q100010 Q99716
## 5563                                                                      
## 5564                                                                      
## 5565                                                                      
## 5566                                                                      
## 5567                                                                      
## 5568                                                                      
##      Q99581 Q99480 Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 5563                                                        
## 5564                                                        
## 5565                                                        
## 5566                                                        
## 5567                                                        
## 5568                                                        
## 'data.frame':    5568 obs. of  20 variables:
##  $ USER_ID        : int  1 4 5 8 9 10 11 12 13 15 ...
##  $ YOB            : int  1938 1970 1997 1983 1984 1997 1983 1996 NA 1981 ...
##  $ Gender         : chr  "Male" "Female" "Male" "Male" ...
##  $ Income         : chr  "" "over $150,000" "$75,000 - $100,000" "$100,001 - $150,000" ...
##  $ HouseholdStatus: chr  "Married (w/kids)" "Domestic Partners (w/kids)" "Single (no kids)" "Married (w/kids)" ...
##  $ EducationLevel : chr  "" "Bachelor's Degree" "High School Diploma" "Bachelor's Degree" ...
##  $ Party          : chr  "Democrat" "Democrat" "Republican" "Democrat" ...
##  $ Q124742        : chr  "No" "" "" "No" ...
##  $ Q124122        : chr  "" "Yes" "Yes" "Yes" ...
##  $ Q123464        : chr  "No" "No" "Yes" "No" ...
##  $ Q123621        : chr  "No" "No" "No" "Yes" ...
##  $ Q122769        : chr  "No" "No" "" "No" ...
##  $ Q122770        : chr  "Yes" "Yes" "Yes" "No" ...
##  $ Q122771        : chr  "Public" "Public" "Private" "Public" ...
##  $ Q122120        : chr  "No" "No" "No" "No" ...
##  $ Q121699        : chr  "Yes" "Yes" "No" "Yes" ...
##  $ Q121700        : chr  "No" "No" "No" "No" ...
##  $ Q120978        : chr  "" "Yes" "Yes" "Yes" ...
##  $ Q121011        : chr  "No" "No" "No" "No" ...
##  $ Q120379        : chr  "No" "No" "No" "No" ...
## NULL
## 'data.frame':    5568 obs. of  20 variables:
##  $ Q120650: chr  "Yes" "Yes" "Yes" "Yes" ...
##  $ Q118117: chr  "Yes" "No" "Yes" "No" ...
##  $ Q118233: chr  "No" "No" "No" "No" ...
##  $ Q118237: chr  "No" "No" "Yes" "No" ...
##  $ Q116441: chr  "No" "Yes" "No" "No" ...
##  $ Q116197: chr  "P.M." "A.M." "A.M." "A.M." ...
##  $ Q115611: chr  "No" "No" "Yes" "No" ...
##  $ Q115899: chr  "Circumstances" "Me" "Circumstances" "Circumstances" ...
##  $ Q115390: chr  "Yes" "Yes" "No" "Yes" ...
##  $ Q114748: chr  "Yes" "No" "No" "No" ...
##  $ Q115195: chr  "Yes" "Yes" "Yes" "Yes" ...
##  $ Q113584: chr  "Technology" "" "Technology" "People" ...
##  $ Q112478: chr  "No" "" "Yes" "Yes" ...
##  $ Q112270: chr  "" "" "Yes" "Yes" ...
##  $ Q111848: chr  "No" "" "No" "Yes" ...
##  $ Q106993: chr  "Yes" "No" "Yes" "Yes" ...
##  $ Q106388: chr  "No" "Yes" "No" "No" ...
##  $ Q105655: chr  "No" "No" "No" "Yes" ...
##  $ Q104996: chr  "Yes" "Yes" "No" "Yes" ...
##  $ Q102674: chr  "No" "" "No" "No" ...
## NULL
## 'data.frame':    5568 obs. of  21 variables:
##  $ Q102674: chr  "No" "" "No" "No" ...
##  $ Q102687: chr  "Yes" "" "Yes" "Yes" ...
##  $ Q102289: chr  "No" "" "No" "Yes" ...
##  $ Q102089: chr  "Own" "" "Own" "Own" ...
##  $ Q101162: chr  "Optimist" "" "Pessimist" "Optimist" ...
##  $ Q101163: chr  "" "" "Mom" "Mom" ...
##  $ Q101596: chr  "Yes" "" "No" "No" ...
##  $ Q100689: chr  "Yes" "" "No" "No" ...
##  $ Q100680: chr  "No" "" "No" "No" ...
##  $ Q100562: chr  "No" "" "No" "Yes" ...
##  $ Q99982 : chr  "Nope" "" "Nope" "Check!" ...
##  $ Q100010: chr  "Yes" "" "Yes" "No" ...
##  $ Q99716 : chr  "No" "" "No" "No" ...
##  $ Q99581 : chr  "No" "" "No" "No" ...
##  $ Q99480 : chr  "" "No" "No" "Yes" ...
##  $ Q98869 : chr  "No" "No" "Yes" "Yes" ...
##  $ Q98578 : chr  "" "No" "No" "No" ...
##  $ Q98059 : chr  "Only-child" "Only-child" "Yes" "Yes" ...
##  $ Q98078 : chr  "No" "Yes" "No" "No" ...
##  $ Q98197 : chr  "No" "No" "Yes" "No" ...
##  $ Q96024 : chr  "Yes" "No" "No" "Yes" ...
## NULL
## Warning in myprint_str_df(obsDf): [list output truncated]
## [1] "Reading file ./data/test2016.csv..."
## [1] "dimensions of data in ./data/test2016.csv: 1,392 rows x 107 cols"
##   USER_ID  YOB Gender             Income   HouseholdStatus
## 1       2 1985 Female  $25,001 - $50,000  Single (no kids)
## 2       3 1983   Male  $50,000 - $74,999  Married (w/kids)
## 3       6 1995   Male $75,000 - $100,000  Single (no kids)
## 4       7 1980 Female  $50,000 - $74,999  Single (no kids)
## 5      14 1980 Female                    Married (no kids)
## 6      28 1973   Male      over $150,000 Married (no kids)
##          EducationLevel Q124742 Q124122 Q123464 Q123621 Q122769 Q122770
## 1       Master's Degree             Yes      No     Yes      No      No
## 2 Current Undergraduate                      No             Yes     Yes
## 3          Current K-12                                                
## 4       Master's Degree     Yes     Yes      No     Yes     Yes     Yes
## 5 Current Undergraduate             Yes      No     Yes      No      No
## 6       Master's Degree      No     Yes      No     Yes      No      No
##   Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650 Q120472
## 1  Public      No     Yes     Yes     Yes      No     Yes     Yes Science
## 2  Public      No     Yes      No                                        
## 3                      No      No      No     Yes      No     Yes Science
## 4  Public      No     Yes      No     Yes      No     Yes     Yes Science
## 5  Public     Yes     Yes      No     Yes     Yes      No     Yes     Art
## 6  Public      No     Yes      No     Yes     Yes     Yes     Yes Science
##       Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892 Q118117
## 1 Study first     Yes     Yes     Yes      No  Giving     Yes      No
## 2 Study first      No     Yes              No                        
## 3   Try first      No     Yes      No     Yes  Giving                
## 4   Try first     Yes      No      No     Yes  Giving     Yes     Yes
## 5   Try first     Yes     Yes     Yes     Yes  Giving      No      No
## 6   Try first     Yes     Yes      No      No  Giving      No     Yes
##      Q118232 Q118233 Q118237     Q117186        Q117193 Q116797 Q116881
## 1   Idealist      No     Yes Cool headed      Odd hours     Yes   Happy
## 2                                                                      
## 3                                                                      
## 4   Idealist      No      No Cool headed Standard hours      No   Happy
## 5   Idealist      No     Yes  Hot headed Standard hours     Yes   Happy
## 6 Pragmatist     Yes      No  Hot headed      Odd hours     Yes   Right
##   Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777 Q115610 Q115611
## 1     Yes     Yes      No     Yes    A.M.     Yes     End     Yes      No
## 2     Yes     Yes                    P.M.                                
## 3     Yes                                                                
## 4     Yes      No      No     Yes    A.M.     Yes   Start     Yes      No
## 5     Yes     Yes     Yes      No    P.M.     Yes     End      No      No
## 6     Yes     Yes     Yes     Yes    P.M.             End     Yes     Yes
##         Q115899 Q115390 Q114961 Q114748 Q115195 Q114517 Q114386 Q113992
## 1            Me      No     Yes      No     Yes     Yes     TMI        
## 2                                            No                     Yes
## 3                   Yes      No     Yes     Yes      No     TMI      No
## 4            Me     Yes      No     Yes     Yes     Yes     TMI      No
## 5            Me      No      No      No     Yes      No     TMI      No
## 6 Circumstances      No     Yes      No     Yes      No     TMI     Yes
##   Q114152 Q113583    Q113584 Q113181 Q112478 Q112512 Q112270 Q111848
## 1      No   Tunes     People     Yes     Yes      No     Yes     Yes
## 2      No                         No                      No     Yes
## 3      No   Tunes Technology     Yes      No     Yes      No        
## 4     Yes    Talk     People      No      No     Yes      No     Yes
## 5           Tunes Technology      No     Yes     Yes             Yes
## 6      No    Talk Technology      No     Yes     Yes      No     Yes
##      Q111580 Q111220 Q110740 Q109367  Q108950 Q109244 Q108855 Q108617
## 1 Supportive      No             Yes Cautious     Yes    Yes!        
## 2                 No             Yes Cautious      No    Yes!      No
## 3                                 No               No              No
## 4 Supportive      No      PC      No Cautious     Yes    Yes!      No
## 5 Supportive     Yes     Mac     Yes Cautious      No    Yes!      No
## 6  Demanding      No      PC     Yes Cautious      No  Umm...      No
##   Q108856 Q108754   Q108342 Q108343 Q107869 Q107491 Q106993     Q106997
## 1             Yes In-person     Yes                                    
## 2   Space      No                       Yes     Yes     Yes Grrr people
## 3             Yes In-person      No      No     Yes     Yes Yay people!
## 4   Space      No    Online      No      No     Yes     Yes Yay people!
## 5   Space      No In-person      No      No     Yes      No Grrr people
## 6   Space      No In-person     Yes             Yes     Yes Grrr people
##   Q106272 Q106388 Q106389 Q106042 Q105840 Q105655 Q104996 Q103293 Q102906
## 1                                                                        
## 2     Yes      No      No     Yes      No     Yes      No      No        
## 3     Yes      No     Yes      No      No     Yes     Yes      No      No
## 4      No      No      No      No      No     Yes     Yes      No      No
## 5      No      No      No     Yes     Yes     Yes     Yes     Yes      No
## 6     Yes      No     Yes     Yes      No      No      No     Yes     Yes
##   Q102674 Q102687 Q102289 Q102089   Q101162 Q101163 Q101596 Q100689
## 1                                                                No
## 2                            Rent Pessimist     Dad                
## 3      No      No     Yes     Own  Optimist     Mom      No      No
## 4      No      No      No     Own  Optimist     Dad      No      No
## 5     Yes      No      No     Own Pessimist     Mom      No     Yes
## 6     Yes     Yes      No     Own Pessimist     Mom      No     Yes
##   Q100680 Q100562 Q99982 Q100010 Q99716 Q99581 Q99480 Q98869 Q98578 Q98059
## 1     Yes     Yes                                        Yes              
## 2             Yes                                        Yes           Yes
## 3     Yes     Yes   Nope      No     No     No    Yes    Yes     No    Yes
## 4     Yes     Yes   Nope     Yes     No     No     No    Yes     No    Yes
## 5     Yes     Yes   Nope     Yes     No     No    Yes     No     No    Yes
## 6     Yes     Yes   Nope     Yes     No     No    Yes     No     No    Yes
##   Q98078 Q98197 Q96024
## 1                     
## 2    Yes     No    Yes
## 3     No    Yes    Yes
## 4     No     No    Yes
## 5     No     No     No
## 6     No     No    Yes
##      USER_ID  YOB Gender              Income   HouseholdStatus
## 503     2555 1956   Male       over $150,000  Married (w/kids)
## 515     2616 1959   Male       over $150,000  Married (w/kids)
## 857     4346 1990 Female   $50,000 - $74,999                  
## 950     4814 1969   Male  $75,000 - $100,000  Married (w/kids)
## 1207    6057 1937 Female   $25,001 - $50,000 Married (no kids)
## 1255    6285 1976 Female $100,001 - $150,000 Married (no kids)
##         EducationLevel Q124742 Q124122 Q123464 Q123621 Q122769 Q122770
## 503  Bachelor's Degree      No      No      No     Yes      No     Yes
## 515  Bachelor's Degree                                                
## 857  Bachelor's Degree                                                
## 950  Bachelor's Degree             Yes      No     Yes      No      No
## 1207 Bachelor's Degree                                      No     Yes
## 1255 Bachelor's Degree                                                
##      Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650
## 503  Private      No     Yes      No      No     Yes      No     Yes
## 515               No      No                                        
## 857               No     Yes      No      No      No      No     Yes
## 950   Public     Yes     Yes      No     Yes     Yes      No     Yes
## 1207  Public      No     Yes      No      No      No              No
## 1255                                                                
##      Q120472     Q120194 Q120012 Q120014 Q119334 Q119851   Q119650 Q118892
## 503  Science Study first      No     Yes      No     Yes    Giving     Yes
## 515                                                                    Yes
## 857  Science Study first      No      No     Yes      No Receiving     Yes
## 950  Science Study first      No      No      No      No    Giving      No
## 1207         Study first      No      No             Yes Receiving     Yes
## 1255                                                                      
##      Q118117    Q118232 Q118233 Q118237     Q117186        Q117193 Q116797
## 503       No Pragmatist      No      No Cool headed Standard hours      No
## 515       No Pragmatist      No     Yes Cool headed Standard hours      No
## 857      Yes Pragmatist      No      No Cool headed      Odd hours      No
## 950       No Pragmatist      No     Yes  Hot headed      Odd hours     Yes
## 1207      No Pragmatist      No      No  Hot headed                     No
## 1255                                                                      
##      Q116881 Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777
## 503    Happy     Yes     Yes      No      No    A.M.     Yes     End
## 515    Right     Yes     Yes      No     Yes             Yes        
## 857    Right     Yes     Yes      No      No    A.M.     Yes   Start
## 950    Happy     Yes     Yes     Yes      No    P.M.     Yes   Start
## 1207   Happy     Yes     Yes      No      No    A.M.     Yes   Start
## 1255                     Yes      No     Yes    A.M.     Yes   Start
##      Q115610 Q115611       Q115899 Q115390 Q114961 Q114748 Q115195 Q114517
## 503      Yes     Yes            Me      No      No      No     Yes     Yes
## 515      Yes      No            Me     Yes      No     Yes     Yes      No
## 857      Yes      No            Me              No      No      No     Yes
## 950      Yes      No            Me     Yes      No     Yes      No      No
## 1207      No      No Circumstances     Yes      No     Yes      No     Yes
## 1255     Yes      No Circumstances      No     Yes      No     Yes     Yes
##         Q114386 Q113992 Q114152 Q113583    Q113584 Q113181 Q112478 Q112512
## 503         TMI     Yes     Yes   Tunes     People     Yes      No     Yes
## 515                  No     Yes    Talk Technology                        
## 857  Mysterious      No      No   Tunes     People      No      No      No
## 950  Mysterious      No      No   Tunes     People     Yes     Yes     Yes
## 1207                Yes      No    Talk                                Yes
## 1255        TMI             Yes                                Yes     Yes
##      Q112270 Q111848    Q111580 Q111220 Q110740 Q109367       Q108950
## 503       No     Yes  Demanding      No      PC      No      Cautious
## 515       No     Yes                 No     Mac     Yes              
## 857      Yes     Yes Supportive      No     Mac      No Risk-friendly
## 950       No     Yes Supportive     Yes      PC      No      Cautious
## 1207                 Supportive      No      PC              Cautious
## 1255     Yes     Yes  Demanding      No     Mac                      
##      Q109244 Q108855 Q108617 Q108856 Q108754   Q108342 Q108343 Q107869
## 503       No  Umm...      No   Space      No In-person      No     Yes
## 515                                                                   
## 857      Yes  Umm...      No   Space      No In-person      No     Yes
## 950       No    Yes!      No   Space      No In-person      No      No
## 1207            Yes!      No   Space      No In-person      No     Yes
## 1255                                                                  
##      Q107491 Q106993     Q106997 Q106272 Q106388 Q106389 Q106042 Q105840
## 503      Yes     Yes Yay people!     Yes      No      No     Yes      No
## 515                                                                   No
## 857       No     Yes Grrr people     Yes      No     Yes      No      No
## 950      Yes      No Grrr people     Yes     Yes      No      No      No
## 1207     Yes     Yes                 Yes                                
## 1255                                                                    
##      Q105655 Q104996 Q103293 Q102906 Q102674 Q102687 Q102289 Q102089
## 503       No     Yes      No      No      No     Yes      No     Own
## 515      Yes     Yes                                                
## 857       No     Yes     Yes      No      No     Yes     Yes     Own
## 950      Yes     Yes     Yes      No      No     Yes      No     Own
## 1207     Yes                                                        
## 1255                                                                
##        Q101162 Q101163 Q101596 Q100689 Q100680 Q100562 Q99982 Q100010
## 503  Pessimist     Mom     Yes     Yes      No     Yes Check!     Yes
## 515                                                    Check!     Yes
## 857   Optimist     Mom      No     Yes     Yes      No   Nope     Yes
## 950  Pessimist     Mom     Yes      No      No      No Check!     Yes
## 1207                                                                 
## 1255                                                                 
##      Q99716 Q99581 Q99480 Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 503      No     No    Yes    Yes     No    Yes    Yes    Yes    Yes
## 515             No    Yes    Yes           Yes     No    Yes    Yes
## 857      No    Yes    Yes    Yes     No    Yes     No     No     No
## 950      No     No    Yes    Yes     No    Yes     No    Yes    Yes
## 1207                                                               
## 1255                                                               
##      USER_ID  YOB Gender              Income             HouseholdStatus
## 1387    6922 1988   Male   $50,000 - $74,999            Single (no kids)
## 1388    6928 1977 Female   $50,000 - $74,999 Domestic Partners (no kids)
## 1389    6930 1998 Female $100,001 - $150,000            Single (no kids)
## 1390    6941 1989   Male   $25,001 - $50,000           Married (no kids)
## 1391    6946 1996   Male                                                
## 1392    6947   NA Female                                                
##         EducationLevel Q124742 Q124122 Q123464 Q123621 Q122769 Q122770
## 1387   Master's Degree                                                
## 1388   Master's Degree                                                
## 1389      Current K-12                                      No      No
## 1390 Bachelor's Degree                                                
## 1391      Current K-12                                                
## 1392                       Yes     Yes      No      No      No      No
##      Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650
## 1387                     Yes     Yes     Yes     Yes     Yes     Yes
## 1388                             Yes              No             Yes
## 1389  Public     Yes     Yes     Yes     Yes     Yes     Yes     Yes
## 1390             Yes     Yes      No      No      No                
## 1391             Yes      No      No     Yes      No     Yes     Yes
## 1392  Public     Yes     Yes      No     Yes     Yes     Yes     Yes
##      Q120472     Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892
## 1387 Science   Try first      No     Yes     Yes      No  Giving        
## 1388     Art                                                            
## 1389     Art Study first     Yes      No     Yes      No  Giving        
## 1390                                                                    
## 1391     Art Study first     Yes     Yes     Yes      No  Giving        
## 1392     Art                  No      No      No     Yes  Giving        
##      Q118117 Q118232 Q118233 Q118237 Q117186 Q117193 Q116797 Q116881
## 1387                                                                
## 1388                                                                
## 1389                                                                
## 1390                                                                
## 1391                                                                
## 1392                                                                
##      Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777 Q115610
## 1387                                                                
## 1388                                                                
## 1389                                                                
## 1390                                                                
## 1391                                                                
## 1392                                                                
##      Q115611 Q115899 Q115390 Q114961 Q114748 Q115195 Q114517 Q114386
## 1387                                                                
## 1388                                                                
## 1389                                                                
## 1390                                                                
## 1391                                                                
## 1392                                                                
##      Q113992 Q114152 Q113583 Q113584 Q113181 Q112478 Q112512 Q112270
## 1387                                                                
## 1388                                                                
## 1389                                                                
## 1390                                                                
## 1391                                                                
## 1392                                                                
##      Q111848 Q111580 Q111220 Q110740 Q109367 Q108950 Q109244 Q108855
## 1387                                                                
## 1388                                                                
## 1389                                                                
## 1390                                                                
## 1391                                                                
## 1392                                                                
##      Q108617 Q108856 Q108754 Q108342 Q108343 Q107869 Q107491 Q106993
## 1387                                                                
## 1388                                                                
## 1389                                                                
## 1390                                                                
## 1391                                                                
## 1392                                                                
##      Q106997 Q106272 Q106388 Q106389 Q106042 Q105840 Q105655 Q104996
## 1387                                                                
## 1388                                                                
## 1389                                                                
## 1390                                                                
## 1391                                                                
## 1392                                                                
##      Q103293 Q102906 Q102674 Q102687 Q102289 Q102089 Q101162 Q101163
## 1387                                                                
## 1388                                                                
## 1389                                                                
## 1390                                                                
## 1391                                                                
## 1392                                                                
##      Q101596 Q100689 Q100680 Q100562 Q99982 Q100010 Q99716 Q99581 Q99480
## 1387                                                                    
## 1388                                                                    
## 1389                                                                    
## 1390                                                                    
## 1391                                                                    
## 1392                                                                    
##      Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 1387                                          
## 1388                                          
## 1389                                          
## 1390                                          
## 1391                                          
## 1392                                          
## 'data.frame':    1392 obs. of  20 variables:
##  $ USER_ID        : int  2 3 6 7 14 28 29 37 44 56 ...
##  $ YOB            : int  1985 1983 1995 1980 1980 1973 1968 1961 1989 1975 ...
##  $ Gender         : chr  "Female" "Male" "Male" "Female" ...
##  $ Income         : chr  "$25,001 - $50,000" "$50,000 - $74,999" "$75,000 - $100,000" "$50,000 - $74,999" ...
##  $ HouseholdStatus: chr  "Single (no kids)" "Married (w/kids)" "Single (no kids)" "Single (no kids)" ...
##  $ EducationLevel : chr  "Master's Degree" "Current Undergraduate" "Current K-12" "Master's Degree" ...
##  $ Q124742        : chr  "" "" "" "Yes" ...
##  $ Q124122        : chr  "Yes" "" "" "Yes" ...
##  $ Q123464        : chr  "No" "No" "" "No" ...
##  $ Q123621        : chr  "Yes" "" "" "Yes" ...
##  $ Q122769        : chr  "No" "Yes" "" "Yes" ...
##  $ Q122770        : chr  "No" "Yes" "" "Yes" ...
##  $ Q122771        : chr  "Public" "Public" "" "Public" ...
##  $ Q122120        : chr  "No" "No" "" "No" ...
##  $ Q121699        : chr  "Yes" "Yes" "No" "Yes" ...
##  $ Q121700        : chr  "Yes" "No" "No" "No" ...
##  $ Q120978        : chr  "Yes" "" "No" "Yes" ...
##  $ Q121011        : chr  "No" "" "Yes" "No" ...
##  $ Q120379        : chr  "Yes" "" "No" "Yes" ...
##  $ Q120650        : chr  "Yes" "" "Yes" "Yes" ...
## NULL
## 'data.frame':    1392 obs. of  20 variables:
##  $ Q120012: chr  "Yes" "No" "No" "Yes" ...
##  $ Q120014: chr  "Yes" "Yes" "Yes" "No" ...
##  $ Q118117: chr  "No" "" "" "Yes" ...
##  $ Q118237: chr  "Yes" "" "" "No" ...
##  $ Q116953: chr  "Yes" "Yes" "Yes" "Yes" ...
##  $ Q116601: chr  "Yes" "Yes" "" "No" ...
##  $ Q116448: chr  "Yes" "" "" "Yes" ...
##  $ Q116197: chr  "A.M." "P.M." "" "A.M." ...
##  $ Q115899: chr  "Me" "" "" "Me" ...
##  $ Q114961: chr  "Yes" "" "No" "No" ...
##  $ Q113584: chr  "People" "" "Technology" "People" ...
##  $ Q113181: chr  "Yes" "No" "Yes" "No" ...
##  $ Q112512: chr  "No" "" "Yes" "Yes" ...
##  $ Q108950: chr  "Cautious" "Cautious" "" "Cautious" ...
##  $ Q108617: chr  "" "No" "No" "No" ...
##  $ Q108342: chr  "In-person" "" "In-person" "Online" ...
##  $ Q107491: chr  "" "Yes" "Yes" "Yes" ...
##  $ Q106272: chr  "" "Yes" "Yes" "No" ...
##  $ Q106389: chr  "" "No" "Yes" "No" ...
##  $ Q104996: chr  "" "No" "Yes" "Yes" ...
## NULL
## 'data.frame':    1392 obs. of  21 variables:
##  $ Q102674: chr  "" "" "No" "No" ...
##  $ Q102687: chr  "" "" "No" "No" ...
##  $ Q102289: chr  "" "" "Yes" "No" ...
##  $ Q102089: chr  "" "Rent" "Own" "Own" ...
##  $ Q101162: chr  "" "Pessimist" "Optimist" "Optimist" ...
##  $ Q101163: chr  "" "Dad" "Mom" "Dad" ...
##  $ Q101596: chr  "" "" "No" "No" ...
##  $ Q100689: chr  "No" "" "No" "No" ...
##  $ Q100680: chr  "Yes" "" "Yes" "Yes" ...
##  $ Q100562: chr  "Yes" "Yes" "Yes" "Yes" ...
##  $ Q99982 : chr  "" "" "Nope" "Nope" ...
##  $ Q100010: chr  "" "" "No" "Yes" ...
##  $ Q99716 : chr  "" "" "No" "No" ...
##  $ Q99581 : chr  "" "" "No" "No" ...
##  $ Q99480 : chr  "" "" "Yes" "No" ...
##  $ Q98869 : chr  "Yes" "Yes" "Yes" "Yes" ...
##  $ Q98578 : chr  "" "" "No" "No" ...
##  $ Q98059 : chr  "" "Yes" "Yes" "Yes" ...
##  $ Q98078 : chr  "" "Yes" "No" "No" ...
##  $ Q98197 : chr  "" "No" "Yes" "No" ...
##  $ Q96024 : chr  "" "Yes" "Yes" "Yes" ...
## NULL
## Warning in myprint_str_df(obsDf): [list output truncated]
## [1] "Creating new feature: .pos..."
## [1] "Creating new feature: YOB.Age.fctr..."
## [1] "Creating new feature: YOB.Age.dff..."
## [1] "Creating new feature: Gender.fctr..."
## [1] "Creating new feature: Income.fctr..."
## [1] "Creating new feature: Hhold.fctr..."
## [1] "Creating new feature: Edn.fctr..."
## [1] "Creating new feature: Q124742.fctr..."
## [1] "Creating new feature: Q124122.fctr..."
## [1] "Creating new feature: Q123621.fctr..."
## [1] "Creating new feature: Q123464.fctr..."
## [1] "Creating new feature: Q122771.fctr..."
## [1] "Creating new feature: Q122770.fctr..."
## [1] "Creating new feature: Q122769.fctr..."
## [1] "Creating new feature: Q122120.fctr..."
## [1] "Creating new feature: Q121700.fctr..."
## [1] "Creating new feature: Q121699.fctr..."
## [1] "Creating new feature: Q121011.fctr..."
## [1] "Creating new feature: Q120978.fctr..."
## [1] "Creating new feature: Q120650.fctr..."
## [1] "Creating new feature: Q120472.fctr..."
## [1] "Creating new feature: Q120379.fctr..."
## [1] "Creating new feature: Q120194.fctr..."
## [1] "Creating new feature: Q120014.fctr..."
## [1] "Creating new feature: Q120012.fctr..."
## [1] "Creating new feature: Q119851.fctr..."
## [1] "Creating new feature: Q119650.fctr..."
## [1] "Creating new feature: Q119334.fctr..."
## [1] "Creating new feature: Q118892.fctr..."
## [1] "Creating new feature: Q118237.fctr..."
## [1] "Creating new feature: Q118233.fctr..."
## [1] "Creating new feature: Q118232.fctr..."
## [1] "Creating new feature: Q118117.fctr..."
## [1] "Creating new feature: Q117193.fctr..."
## [1] "Creating new feature: Q117186.fctr..."
## [1] "Creating new feature: Q116797.fctr..."
## [1] "Creating new feature: Q116881.fctr..."
## [1] "Creating new feature: Q116953.fctr..."
## [1] "Creating new feature: Q116601.fctr..."
## [1] "Creating new feature: Q116441.fctr..."
## [1] "Creating new feature: Q116448.fctr..."
## [1] "Creating new feature: Q116197.fctr..."
## [1] "Creating new feature: Q115602.fctr..."
## [1] "Creating new feature: Q115777.fctr..."
## [1] "Creating new feature: Q115610.fctr..."
## [1] "Creating new feature: Q115611.fctr..."
## [1] "Creating new feature: Q115899.fctr..."
## [1] "Creating new feature: Q115390.fctr..."
## [1] "Creating new feature: Q115195.fctr..."
## [1] "Creating new feature: Q114961.fctr..."
## [1] "Creating new feature: Q114748.fctr..."
## [1] "Creating new feature: Q114517.fctr..."
## [1] "Creating new feature: Q114386.fctr..."
## [1] "Creating new feature: Q114152.fctr..."
## [1] "Creating new feature: Q113992.fctr..."
## [1] "Creating new feature: Q113583.fctr..."
## [1] "Creating new feature: Q113584.fctr..."
## [1] "Creating new feature: Q113181.fctr..."
## [1] "Creating new feature: Q112478.fctr..."
## [1] "Creating new feature: Q112512.fctr..."
## [1] "Creating new feature: Q112270.fctr..."
## [1] "Creating new feature: Q111848.fctr..."
## [1] "Creating new feature: Q111580.fctr..."
## [1] "Creating new feature: Q111220.fctr..."
## [1] "Creating new feature: Q110740.fctr..."
## [1] "Creating new feature: Q109367.fctr..."
## [1] "Creating new feature: Q109244.fctr..."
## [1] "Creating new feature: Q108950.fctr..."
## [1] "Creating new feature: Q108855.fctr..."
## [1] "Creating new feature: Q108617.fctr..."
## [1] "Creating new feature: Q108856.fctr..."
## [1] "Creating new feature: Q108754.fctr..."
## [1] "Creating new feature: Q108342.fctr..."
## [1] "Creating new feature: Q108343.fctr..."
## [1] "Creating new feature: Q107869.fctr..."
## [1] "Creating new feature: Q107491.fctr..."
## [1] "Creating new feature: Q106993.fctr..."
## [1] "Creating new feature: Q106997.fctr..."
## [1] "Creating new feature: Q106272.fctr..."
## [1] "Creating new feature: Q106388.fctr..."
## [1] "Creating new feature: Q106389.fctr..."
## [1] "Creating new feature: Q106042.fctr..."
## [1] "Creating new feature: Q105840.fctr..."
## [1] "Creating new feature: Q105655.fctr..."
## [1] "Creating new feature: Q104996.fctr..."
## [1] "Creating new feature: Q103293.fctr..."
## [1] "Creating new feature: Q102906.fctr..."
## [1] "Creating new feature: Q102674.fctr..."
## [1] "Creating new feature: Q102687.fctr..."
## [1] "Creating new feature: Q102289.fctr..."
## [1] "Creating new feature: Q102089.fctr..."
## [1] "Creating new feature: Q101162.fctr..."
## [1] "Creating new feature: Q101163.fctr..."
## [1] "Creating new feature: Q101596.fctr..."
## [1] "Creating new feature: Q100689.fctr..."
## [1] "Creating new feature: Q100680.fctr..."
## [1] "Creating new feature: Q100562.fctr..."
## [1] "Creating new feature: Q100010.fctr..."
## [1] "Creating new feature: Q99982.fctr..."
## [1] "Creating new feature: Q99716.fctr..."
## [1] "Creating new feature: Q99581.fctr..."
## [1] "Creating new feature: Q99480.fctr..."
## [1] "Creating new feature: Q98869.fctr..."
## [1] "Creating new feature: Q98578.fctr..."
## [1] "Creating new feature: Q98197.fctr..."
## [1] "Creating new feature: Q98059.fctr..."
## [1] "Creating new feature: Q98078.fctr..."
## [1] "Creating new feature: Q96024.fctr..."
## [1] "Partition stats:"
## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk
##        Party  .src   .n
## 1   Democrat Train 2951
## 2 Republican Train 2617
## 3       <NA>  Test 1392
##        Party  .src   .n
## 1   Democrat Train 2951
## 2 Republican Train 2617
## 3       <NA>  Test 1392
## Loading required package: RColorBrewer

##    .src   .n
## 1 Train 5568
## 2  Test 1392
## [1] "Running glbObsDropCondition filter: (glbObsAll[, \"Q109244\"] != \"\")"
## [1] "Partition stats:"
##        Party  .src   .n
## 1   Democrat Train 1171
## 2 Republican Train 1013
## 3       <NA>  Test  547
##        Party  .src   .n
## 1   Democrat Train 1171
## 2 Republican Train 1013
## 3       <NA>  Test  547

##    .src   .n
## 1 Train 2184
## 2  Test  547
## Loading required package: lazyeval
## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
## 
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
## 
## Attaching package: 'gdata'
## The following objects are masked from 'package:dplyr':
## 
##     combine, first, last
## The following object is masked from 'package:stats':
## 
##     nobs
## The following object is masked from 'package:utils':
## 
##     object.size
## [1] "Found 0 duplicates by all features:"
## NULL
##          label step_major step_minor label_minor    bgn    end elapsed
## 1  import.data          1          0           0  7.843 19.975  12.132
## 2 inspect.data          2          0           0 19.975     NA      NA

Step 2.0: inspect data

## Warning: Removed 547 rows containing non-finite values (stat_count).
## Loading required package: reshape2

##       Party.Democrat Party.Republican Party.NA
## Test              NA               NA      547
## Train           1171             1013       NA
##       Party.Democrat Party.Republican Party.NA
## Test              NA               NA        1
## Train      0.5361722        0.4638278       NA
## [1] "numeric data missing in : "
## YOB 
## 239 
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff 
##         253 
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
##          Gender          Income HouseholdStatus  EducationLevel 
##              88             665             316             521 
##           Party         Q124742         Q124122         Q123464 
##              NA            2313            1954            1895 
##         Q123621         Q122769         Q122770         Q122771 
##            1928            1855            1758            1745 
##         Q122120         Q121699         Q121700         Q120978 
##            1719            1508            1543            1447 
##         Q121011         Q120379         Q120650         Q120472 
##            1447            1485            1367            1504 
##         Q120194         Q120012         Q120014         Q119334 
##            1657            1488            1641            1652 
##         Q119851         Q119650         Q118892         Q118117 
##            1458            1522            1493            1635 
##         Q118232         Q118233         Q118237         Q117186 
##            2006            1837            1789            1914 
##         Q117193         Q116797         Q116881         Q116953 
##            1868            1939            1978            1953 
##         Q116601         Q116441         Q116448         Q116197 
##            1847            1904            1927            1858 
##         Q115602         Q115777         Q115610         Q115611 
##            1844            1943            1870            1754 
##         Q115899         Q115390         Q114961         Q114748 
##            1956            1962            1905            1808 
##         Q115195         Q114517         Q114386         Q113992 
##            1880            1870            1951            1849 
##         Q114152         Q113583         Q113584         Q113181 
##            2031            1883            1901            1906 
##         Q112478         Q112512         Q112270         Q111848 
##            2067            2004            2050            1872 
##         Q111580         Q111220         Q110740         Q109367 
##            1993            1993            1949            2383 
##         Q108950         Q109244         Q108855         Q108617 
##            2346            2731            2379            2265 
##         Q108856         Q108754         Q108342         Q108343 
##            2388            2284            2262            2241 
##         Q107869         Q107491         Q106993         Q106997 
##            2177            2125            2100            2113 
##         Q106272         Q106388         Q106389         Q106042 
##            2084            2114            2140            2093 
##         Q105840         Q105655         Q104996         Q103293 
##            2167            2042            2019            2042 
##         Q102906         Q102674         Q102687         Q102289 
##            2116            2116            2038            2079 
##         Q102089         Q101162         Q101163         Q101596 
##            2052            2094            2152            2104 
##         Q100689         Q100680         Q100562          Q99982 
##            1969            2074            2080            2114 
##         Q100010          Q99716          Q99581          Q99480 
##            2033            2071            2010            2016 
##          Q98869          Q98578          Q98059          Q98078 
##            2088            2073            1984            2125 
##          Q98197          Q96024 
##            2084            2065
##        Party Party.fctr   .n
## 1   Democrat          D 1171
## 2 Republican          R 1013
## 3       <NA>       <NA>  547
## Warning: Removed 1 rows containing missing values (position_stack).

##       Party.fctr.D Party.fctr.R Party.fctr.NA
## Test            NA           NA           547
## Train         1171         1013            NA
##       Party.fctr.D Party.fctr.R Party.fctr.NA
## Test            NA           NA             1
## Train    0.5361722    0.4638278            NA

## [1] "elapsed Time (secs): 9.169000"
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.

## [1] "elapsed Time (secs): 139.788000"
## [1] "elapsed Time (secs): 139.788000"
##          label step_major step_minor label_minor     bgn     end elapsed
## 2 inspect.data          2          0           0  19.975 176.308 156.333
## 3   scrub.data          2          1           1 176.309      NA      NA

Step 2.1: scrub data

```{r scrub.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}

## [1] "numeric data missing in : "
##        YOB Party.fctr 
##        239        547 
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff 
##         253 
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
##          Gender          Income HouseholdStatus  EducationLevel 
##              88             665             316             521 
##           Party         Q124742         Q124122         Q123464 
##              NA            2313            1954            1895 
##         Q123621         Q122769         Q122770         Q122771 
##            1928            1855            1758            1745 
##         Q122120         Q121699         Q121700         Q120978 
##            1719            1508            1543            1447 
##         Q121011         Q120379         Q120650         Q120472 
##            1447            1485            1367            1504 
##         Q120194         Q120012         Q120014         Q119334 
##            1657            1488            1641            1652 
##         Q119851         Q119650         Q118892         Q118117 
##            1458            1522            1493            1635 
##         Q118232         Q118233         Q118237         Q117186 
##            2006            1837            1789            1914 
##         Q117193         Q116797         Q116881         Q116953 
##            1868            1939            1978            1953 
##         Q116601         Q116441         Q116448         Q116197 
##            1847            1904            1927            1858 
##         Q115602         Q115777         Q115610         Q115611 
##            1844            1943            1870            1754 
##         Q115899         Q115390         Q114961         Q114748 
##            1956            1962            1905            1808 
##         Q115195         Q114517         Q114386         Q113992 
##            1880            1870            1951            1849 
##         Q114152         Q113583         Q113584         Q113181 
##            2031            1883            1901            1906 
##         Q112478         Q112512         Q112270         Q111848 
##            2067            2004            2050            1872 
##         Q111580         Q111220         Q110740         Q109367 
##            1993            1993            1949            2383 
##         Q108950         Q109244         Q108855         Q108617 
##            2346            2731            2379            2265 
##         Q108856         Q108754         Q108342         Q108343 
##            2388            2284            2262            2241 
##         Q107869         Q107491         Q106993         Q106997 
##            2177            2125            2100            2113 
##         Q106272         Q106388         Q106389         Q106042 
##            2084            2114            2140            2093 
##         Q105840         Q105655         Q104996         Q103293 
##            2167            2042            2019            2042 
##         Q102906         Q102674         Q102687         Q102289 
##            2116            2116            2038            2079 
##         Q102089         Q101162         Q101163         Q101596 
##            2052            2094            2152            2104 
##         Q100689         Q100680         Q100562          Q99982 
##            1969            2074            2080            2114 
##         Q100010          Q99716          Q99581          Q99480 
##            2033            2071            2010            2016 
##          Q98869          Q98578          Q98059          Q98078 
##            2088            2073            1984            2125 
##          Q98197          Q96024 
##            2084            2065
##            label step_major step_minor label_minor     bgn     end elapsed
## 3     scrub.data          2          1           1 176.309 222.429  46.121
## 4 transform.data          2          2           2 222.430      NA      NA

Step 2.2: transform data

##              label step_major step_minor label_minor     bgn     end
## 4   transform.data          2          2           2 222.430 222.473
## 5 extract.features          3          0           0 222.474      NA
##   elapsed
## 4   0.044
## 5      NA

Step 3.0: extract features

##                       label step_major step_minor label_minor     bgn
## 5          extract.features          3          0           0 222.474
## 6 extract.features.datetime          3          1           1 222.496
##       end elapsed
## 5 222.495   0.021
## 6      NA      NA

Step 3.1: extract features datetime

##                           label step_major step_minor label_minor     bgn
## 1 extract.features.datetime.bgn          1          0           0 222.525
##   end elapsed
## 1  NA      NA
##                       label step_major step_minor label_minor     bgn
## 6 extract.features.datetime          3          1           1 222.496
## 7    extract.features.image          3          2           2 222.539
##       end elapsed
## 6 222.538   0.042
## 7      NA      NA

Step 3.2: extract features image

```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}

##                        label step_major step_minor label_minor     bgn end
## 1 extract.features.image.bgn          1          0           0 222.574  NA
##   elapsed
## 1      NA
##                        label step_major step_minor label_minor     bgn
## 1 extract.features.image.bgn          1          0           0 222.574
## 2 extract.features.image.end          2          0           0 222.584
##       end elapsed
## 1 222.583    0.01
## 2      NA      NA
##                        label step_major step_minor label_minor     bgn
## 1 extract.features.image.bgn          1          0           0 222.574
## 2 extract.features.image.end          2          0           0 222.584
##       end elapsed
## 1 222.583    0.01
## 2      NA      NA
##                    label step_major step_minor label_minor     bgn     end
## 7 extract.features.image          3          2           2 222.539 222.595
## 8 extract.features.price          3          3           3 222.595      NA
##   elapsed
## 7   0.056
## 8      NA

Step 3.3: extract features price

##                        label step_major step_minor label_minor     bgn end
## 1 extract.features.price.bgn          1          0           0 222.623  NA
##   elapsed
## 1      NA
##                    label step_major step_minor label_minor     bgn     end
## 8 extract.features.price          3          3           3 222.595 222.632
## 9  extract.features.text          3          4           4 222.633      NA
##   elapsed
## 8   0.037
## 9      NA

Step 3.4: extract features text

##                       label step_major step_minor label_minor    bgn end
## 1 extract.features.text.bgn          1          0           0 222.68  NA
##   elapsed
## 1      NA
## Warning in rm(tmp_allobs_df): object 'tmp_allobs_df' not found
## Warning in rm(tmp_trnobs_df): object 'tmp_trnobs_df' not found
##                      label step_major step_minor label_minor     bgn
## 9    extract.features.text          3          4           4 222.633
## 10 extract.features.string          3          5           5 222.695
##        end elapsed
## 9  222.695   0.062
## 10      NA      NA

Step 3.5: extract features string

##                         label step_major step_minor label_minor     bgn
## 1 extract.features.string.bgn          1          0           0 222.733
##   end elapsed
## 1  NA      NA
##                                       label step_major step_minor
## 1               extract.features.string.bgn          1          0
## 2 extract.features.stringfactorize.str.vars          2          0
##   label_minor     bgn     end elapsed
## 1           0 222.733 222.742   0.009
## 2           0 222.742      NA      NA
##            Gender            Income   HouseholdStatus    EducationLevel 
##          "Gender"          "Income" "HouseholdStatus"  "EducationLevel" 
##             Party           Q124742           Q124122           Q123464 
##           "Party"         "Q124742"         "Q124122"         "Q123464" 
##           Q123621           Q122769           Q122770           Q122771 
##         "Q123621"         "Q122769"         "Q122770"         "Q122771" 
##           Q122120           Q121699           Q121700           Q120978 
##         "Q122120"         "Q121699"         "Q121700"         "Q120978" 
##           Q121011           Q120379           Q120650           Q120472 
##         "Q121011"         "Q120379"         "Q120650"         "Q120472" 
##           Q120194           Q120012           Q120014           Q119334 
##         "Q120194"         "Q120012"         "Q120014"         "Q119334" 
##           Q119851           Q119650           Q118892           Q118117 
##         "Q119851"         "Q119650"         "Q118892"         "Q118117" 
##           Q118232           Q118233           Q118237           Q117186 
##         "Q118232"         "Q118233"         "Q118237"         "Q117186" 
##           Q117193           Q116797           Q116881           Q116953 
##         "Q117193"         "Q116797"         "Q116881"         "Q116953" 
##           Q116601           Q116441           Q116448           Q116197 
##         "Q116601"         "Q116441"         "Q116448"         "Q116197" 
##           Q115602           Q115777           Q115610           Q115611 
##         "Q115602"         "Q115777"         "Q115610"         "Q115611" 
##           Q115899           Q115390           Q114961           Q114748 
##         "Q115899"         "Q115390"         "Q114961"         "Q114748" 
##           Q115195           Q114517           Q114386           Q113992 
##         "Q115195"         "Q114517"         "Q114386"         "Q113992" 
##           Q114152           Q113583           Q113584           Q113181 
##         "Q114152"         "Q113583"         "Q113584"         "Q113181" 
##           Q112478           Q112512           Q112270           Q111848 
##         "Q112478"         "Q112512"         "Q112270"         "Q111848" 
##           Q111580           Q111220           Q110740           Q109367 
##         "Q111580"         "Q111220"         "Q110740"         "Q109367" 
##           Q108950           Q109244           Q108855           Q108617 
##         "Q108950"         "Q109244"         "Q108855"         "Q108617" 
##           Q108856           Q108754           Q108342           Q108343 
##         "Q108856"         "Q108754"         "Q108342"         "Q108343" 
##           Q107869           Q107491           Q106993           Q106997 
##         "Q107869"         "Q107491"         "Q106993"         "Q106997" 
##           Q106272           Q106388           Q106389           Q106042 
##         "Q106272"         "Q106388"         "Q106389"         "Q106042" 
##           Q105840           Q105655           Q104996           Q103293 
##         "Q105840"         "Q105655"         "Q104996"         "Q103293" 
##           Q102906           Q102674           Q102687           Q102289 
##         "Q102906"         "Q102674"         "Q102687"         "Q102289" 
##           Q102089           Q101162           Q101163           Q101596 
##         "Q102089"         "Q101162"         "Q101163"         "Q101596" 
##           Q100689           Q100680           Q100562            Q99982 
##         "Q100689"         "Q100680"         "Q100562"          "Q99982" 
##           Q100010            Q99716            Q99581            Q99480 
##         "Q100010"          "Q99716"          "Q99581"          "Q99480" 
##            Q98869            Q98578            Q98059            Q98078 
##          "Q98869"          "Q98578"          "Q98059"          "Q98078" 
##            Q98197            Q96024              .src 
##          "Q98197"          "Q96024"            ".src"
##                      label step_major step_minor label_minor     bgn
## 10 extract.features.string          3          5           5 222.695
## 11    extract.features.end          3          6           6 222.765
##        end elapsed
## 10 222.765    0.07
## 11      NA      NA

Step 3.6: extract features end

## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0

##                   label step_major step_minor label_minor     bgn     end
## 11 extract.features.end          3          6           6 222.765 223.705
## 12  manage.missing.data          4          0           0 223.706      NA
##    elapsed
## 11    0.94
## 12      NA

Step 4.0: manage missing data

## [1] "numeric data missing in : "
##        YOB Party.fctr 
##        239        547 
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff 
##         253 
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
##          Gender          Income HouseholdStatus  EducationLevel 
##              88             665             316             521 
##           Party         Q124742         Q124122         Q123464 
##              NA            2313            1954            1895 
##         Q123621         Q122769         Q122770         Q122771 
##            1928            1855            1758            1745 
##         Q122120         Q121699         Q121700         Q120978 
##            1719            1508            1543            1447 
##         Q121011         Q120379         Q120650         Q120472 
##            1447            1485            1367            1504 
##         Q120194         Q120012         Q120014         Q119334 
##            1657            1488            1641            1652 
##         Q119851         Q119650         Q118892         Q118117 
##            1458            1522            1493            1635 
##         Q118232         Q118233         Q118237         Q117186 
##            2006            1837            1789            1914 
##         Q117193         Q116797         Q116881         Q116953 
##            1868            1939            1978            1953 
##         Q116601         Q116441         Q116448         Q116197 
##            1847            1904            1927            1858 
##         Q115602         Q115777         Q115610         Q115611 
##            1844            1943            1870            1754 
##         Q115899         Q115390         Q114961         Q114748 
##            1956            1962            1905            1808 
##         Q115195         Q114517         Q114386         Q113992 
##            1880            1870            1951            1849 
##         Q114152         Q113583         Q113584         Q113181 
##            2031            1883            1901            1906 
##         Q112478         Q112512         Q112270         Q111848 
##            2067            2004            2050            1872 
##         Q111580         Q111220         Q110740         Q109367 
##            1993            1993            1949            2383 
##         Q108950         Q109244         Q108855         Q108617 
##            2346            2731            2379            2265 
##         Q108856         Q108754         Q108342         Q108343 
##            2388            2284            2262            2241 
##         Q107869         Q107491         Q106993         Q106997 
##            2177            2125            2100            2113 
##         Q106272         Q106388         Q106389         Q106042 
##            2084            2114            2140            2093 
##         Q105840         Q105655         Q104996         Q103293 
##            2167            2042            2019            2042 
##         Q102906         Q102674         Q102687         Q102289 
##            2116            2116            2038            2079 
##         Q102089         Q101162         Q101163         Q101596 
##            2052            2094            2152            2104 
##         Q100689         Q100680         Q100562          Q99982 
##            1969            2074            2080            2114 
##         Q100010          Q99716          Q99581          Q99480 
##            2033            2071            2010            2016 
##          Q98869          Q98578          Q98059          Q98078 
##            2088            2073            1984            2125 
##          Q98197          Q96024 
##            2084            2065
## [1] "numeric data missing in : "
##        YOB Party.fctr 
##        239        547 
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff 
##         253 
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
##          Gender          Income HouseholdStatus  EducationLevel 
##              88             665             316             521 
##           Party         Q124742         Q124122         Q123464 
##              NA            2313            1954            1895 
##         Q123621         Q122769         Q122770         Q122771 
##            1928            1855            1758            1745 
##         Q122120         Q121699         Q121700         Q120978 
##            1719            1508            1543            1447 
##         Q121011         Q120379         Q120650         Q120472 
##            1447            1485            1367            1504 
##         Q120194         Q120012         Q120014         Q119334 
##            1657            1488            1641            1652 
##         Q119851         Q119650         Q118892         Q118117 
##            1458            1522            1493            1635 
##         Q118232         Q118233         Q118237         Q117186 
##            2006            1837            1789            1914 
##         Q117193         Q116797         Q116881         Q116953 
##            1868            1939            1978            1953 
##         Q116601         Q116441         Q116448         Q116197 
##            1847            1904            1927            1858 
##         Q115602         Q115777         Q115610         Q115611 
##            1844            1943            1870            1754 
##         Q115899         Q115390         Q114961         Q114748 
##            1956            1962            1905            1808 
##         Q115195         Q114517         Q114386         Q113992 
##            1880            1870            1951            1849 
##         Q114152         Q113583         Q113584         Q113181 
##            2031            1883            1901            1906 
##         Q112478         Q112512         Q112270         Q111848 
##            2067            2004            2050            1872 
##         Q111580         Q111220         Q110740         Q109367 
##            1993            1993            1949            2383 
##         Q108950         Q109244         Q108855         Q108617 
##            2346            2731            2379            2265 
##         Q108856         Q108754         Q108342         Q108343 
##            2388            2284            2262            2241 
##         Q107869         Q107491         Q106993         Q106997 
##            2177            2125            2100            2113 
##         Q106272         Q106388         Q106389         Q106042 
##            2084            2114            2140            2093 
##         Q105840         Q105655         Q104996         Q103293 
##            2167            2042            2019            2042 
##         Q102906         Q102674         Q102687         Q102289 
##            2116            2116            2038            2079 
##         Q102089         Q101162         Q101163         Q101596 
##            2052            2094            2152            2104 
##         Q100689         Q100680         Q100562          Q99982 
##            1969            2074            2080            2114 
##         Q100010          Q99716          Q99581          Q99480 
##            2033            2071            2010            2016 
##          Q98869          Q98578          Q98059          Q98078 
##            2088            2073            1984            2125 
##          Q98197          Q96024 
##            2084            2065
##                  label step_major step_minor label_minor     bgn     end
## 12 manage.missing.data          4          0           0 223.706 224.368
## 13        cluster.data          5          0           0 224.369      NA
##    elapsed
## 12   0.662
## 13      NA

Step 5.0: cluster data

```{r cluster.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}

## Loading required package: proxy
## 
## Attaching package: 'proxy'
## The following objects are masked from 'package:stats':
## 
##     as.dist, dist
## The following object is masked from 'package:base':
## 
##     as.matrix
## Loading required package: dynamicTreeCut
## Loading required package: entropy
## Loading required package: tidyr
## Loading required package: ggdendro
## [1] "Clustering features: "
## Warning in cor(data.matrix(glbObsAll[glbObsAll$.src == "Train",
## glbFeatsCluster]), : the standard deviation is zero
##               abs.cor.y
## Q113181.fctr 0.04357559
## Q102089.fctr 0.04804567
## Q100689.fctr 0.05185690
## Q113583.fctr 0.05306280
## Q101163.fctr 0.07163663
## [1] "    .rnorm cor: 0.0268"
## [1] "  Clustering entropy measure: Party.fctr"
## [1] "glbObsAll Entropy: 0.6905"
##   Hhold.fctr .clusterid Hhold.fctr.clusterid   D   R  .entropy .knt
## 1          N          1                  N_1 131 126 0.6929579  257
## 2        MKn          1                MKn_1 124 104 0.6892949  228
## 3        MKy          1                MKy_1 260 269 0.6930024  529
## 4        PKn          1                PKn_1  46  21 0.6218199   67
## 5        PKy          1                PKy_1  12  18 0.6730117   30
## 6        SKn          1                SKn_1 561 442 0.6860924 1003
## 7        SKy          1                SKy_1  37  33 0.6915136   70
## [1] "glbObsAll$Hhold.fctr Entropy: 0.6869 (99.4790 pct)"
## [1] "Category: N"
## [1] "max distance(0.9804) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4666    5825          R          N           NA           NA           NA
## 6844    6410       <NA>          N           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4666           NA           NA           NA           NA           NA
## 6844           NA           Pc          Yes           No          Yes
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4666           NA           NA           NA           NA           NA
## 6844          Yes           No          Yes          Yes          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4666           NA           NA           NA           NA           NA
## 6844      Science           No    Try first           No          Yes
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4666           NA           NA           NA           NA           NA
## 6844           No    Receiving          Yes           No          Yes
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 4666           NA           NA           NA             NA           NA
## 6844          Yes           Id          Yes Standard hours  Cool headed
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4666           NA           NA           NA           NA           NA
## 6844           No        Happy           No          Yes           No
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4666           NA           NA           NA           NA           NA
## 6844          Yes         A.M.           No        Start          Yes
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4666           NA           NA           NA           NA           NA
## 6844          Yes           Cs          Yes          Yes          Yes
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4666           NA           NA           NA           NA           NA
## 6844          Yes           No          TMI          Yes           No
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4666           NA           NA           No          Yes          Yes
## 6844           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4666           NA           No   Supportive          Yes          Mac
## 6844           NA           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 4666           NA           NA           NA           NA           NA
## 6844           NA           NA           NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4666           NA           NA           NA           NA           No
## 6844           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4666          Yes          Yes           Gr          Yes           No
## 6844           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4666           No          Yes          Yes           No          Yes
## 6844           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4666          Yes           NA           NA           NA           NA
## 6844           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4666           NA    Pessimist          Dad          Yes          Yes
## 6844           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4666          Yes           No          Yes          NA          No
## 6844           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4666         Yes          NA          NA          No          No
## 6844          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 4666         Yes          NA          No
## 6844          NA          NA          NA
## [1] "min distance(0.9658) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4230    5278          D          N           NA           NA           NA
## 4365    5451          R          N           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4230           NA           NA           NA           NA           NA
## 4365           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4230           NA           NA           NA           NA           NA
## 4365           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4230           NA           NA           NA           NA           NA
## 4365           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4230           NA           NA           NA           NA           NA
## 4365           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 4230           NA           NA           NA           NA           NA
## 4365           NA           NA           NA    Odd hours  Cool headed
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4230           NA           NA           NA           NA           NA
## 4365           NA           NA          Yes           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4230           NA           NA           NA           NA           NA
## 4365           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4230           NA           NA           NA           NA           NA
## 4365           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4230           NA           NA           NA           NA           NA
## 4365           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4230           NA           NA           NA           NA           NA
## 4365           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4230           NA           NA           NA           NA           NA
## 4365           NA           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 4230          Yes           NA           NA           NA           NA
## 4365           NA           NA           NA         Yes!           No
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4230        Space          Yes           NA           NA           NA
## 4365    Socialize          Yes           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4230           NA          Yes           Gr           NA          Yes
## 4365           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4230           NA           NA           NA           NA           NA
## 4365           NA           NA           NA           NA          Yes
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4230           No           NA          Yes           No           NA
## 4365           No          Yes           No          Yes           No
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4230         Rent    Pessimist          Mom           NA           No
## 4365         Rent     Optimist          Mom           No          Yes
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4230           No          Yes          Yes          NA          No
## 4365          Yes          Yes           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4230          No          NA          NA          NA          NA
## 4365          No         Yes          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 4230          NA          NA          NA
## 4365          NA          NA          NA
## [1] "Category: MKn"
## [1] "max distance(0.9806) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4905    6129          D        MKn           NA          Yes           NA
## 5820    1301       <NA>        MKn           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4905           NA           NA           NA           NA           NA
## 5820           NA           NA           NA           NA          Yes
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4905           NA           NA          Yes          Yes          Yes
## 5820           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4905      Science          Yes  Study first          Yes           No
## 5820           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4905          Yes       Giving          Yes          Yes           No
## 5820           NA           NA          Yes           No           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 4905           No           Pr           No    Odd hours  Cool headed
## 5820           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4905           No        Right          Yes          Yes          Yes
## 5820           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4905           No         A.M.          Yes        Start          Yes
## 5820           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4905          Yes           Me          Yes          Yes           No
## 5820           NA           NA           NA           NA           No
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4905          Yes           No          TMI           No           No
## 5820           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4905           NA           NA           NA           NA           NA
## 5820         Talk   Technology          Yes          Yes          Yes
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4905           NA           NA    Demanding          Yes           NA
## 5820          Yes          Yes   Supportive           NA           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 4905           NA           NA     Cautious         Yes!           No
## 5820           NA           NA           NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4905    Socialize           No       Online          Yes          Yes
## 5820           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4905          Yes          Yes           Yy           NA           NA
## 5820           NA           NA           NA          Yes           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4905           NA           NA           NA           NA           NA
## 5820           NA          Yes           NA          Yes           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4905           NA           NA           NA           NA           NA
## 5820           NA           NA           NA           NA          Yes
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4905           NA           NA           NA           NA           NA
## 5820         Rent           NA           NA          Yes           No
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4905           NA           NA           NA          NA          NA
## 5820           NA          Yes           NA          NA         Yes
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4905          NA          NA          NA          NA          NA
## 5820          No         Yes         Yes          NA         Yes
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 4905          NA          NA          NA
## 5820         Yes          NA         Yes
## [1] "min distance(0.9669) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4058    5057          D        MKn           NA           NA           NA
## 6396    4209       <NA>        MKn           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4058           NA           NA           NA           NA           NA
## 6396           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4058           NA           NA           NA           NA           NA
## 6396           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4058           NA           NA           NA           NA           NA
## 6396           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4058           NA           NA           NA           NA           NA
## 6396           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 4058           NA           NA           NA           NA           NA
## 6396           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4058           NA           NA           NA           NA           NA
## 6396          Yes           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4058           NA           NA           NA           NA           NA
## 6396           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4058           NA           NA           NA           NA           NA
## 6396           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4058           NA           NA           NA           NA           NA
## 6396           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4058         Talk   Technology           No           No           No
## 6396           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4058          Yes           No   Supportive           No           NA
## 6396           NA           NA           NA           No           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 4058           NA           NA           NA           NA           NA
## 6396          Yes           NA     Cautious           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4058           NA           NA           NA           NA           NA
## 6396           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4058           NA           NA           NA           NA           NA
## 6396           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4058           NA           NA           NA           NA           NA
## 6396           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4058           NA           NA           NA           NA           NA
## 6396           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4058           NA     Optimist          Mom           NA           No
## 6396           NA           NA          Mom           NA           No
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4058          Yes          Yes          Yes      Check!          No
## 6396           No          Yes          Yes          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4058          No          No         Yes         Yes          No
## 6396          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 4058  Only-child          No          No
## 6396          NA          NA          NA
## [1] "Category: MKy"
## [1] "max distance(0.9808) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1621    2008          D        MKy           NA           NA           NA
## 4029    5022          R        MKy          Yes          Yes          Yes
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1621           NA           NA           NA           NA           NA
## 4029           NA           Pc           No           NA           No
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1621           NA           NA           NA           NA           NA
## 4029           NA           NA           NA          Yes          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1621           NA           NA           NA           No           No
## 4029           NA           No  Study first          Yes           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1621          Yes    Receiving           No           NA          Yes
## 4029           NA    Receiving           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 1621           No           Id           No Standard hours  Cool headed
## 4029           NA           NA           NA             NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1621          Yes        Right          Yes          Yes           No
## 4029           NA        Happy           NA           NA           No
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1621           No         P.M.           No          End          Yes
## 4029           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1621           No           Me           NA           NA           NA
## 4029           NA           NA           NA          Yes           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1621           NA           NA           NA           NA           NA
## 4029           NA           NA           NA           NA          Yes
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1621           NA           NA           NA           NA           NA
## 4029           NA           NA          Yes           NA          Yes
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1621           NA           NA           NA           NA           NA
## 4029          Yes           NA           NA           NA          Mac
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 1621           NA           NA Risk-friendly         Yes!           No
## 4029           NA           NA            NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1621    Socialize           No       Online          Yes           No
## 4029           NA          Yes           NA          Yes           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1621          Yes          Yes           Yy          Yes           No
## 4029           NA          Yes           NA           NA           No
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1621          Yes          Yes          Yes          Yes           No
## 4029           NA           No           NA           No          Yes
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1621          Yes          Yes           NA           NA           NA
## 4029           NA           NA           NA          Yes           No
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1621           NA           NA           NA           NA           NA
## 4029           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1621           NA           NA           NA          NA          NA
## 4029           NA           NA           NA          NA          No
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1621          NA          NA          NA          NA          No
## 4029          NA         Yes          NA          No          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 1621         Yes         Yes         Yes
## 4029          NA          NA          NA
## [1] "min distance(0.9660) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1257    1556          D        MKy           NA           NA           NA
## 4320    5395          D        MKy           NA           NA          Yes
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1257           NA           NA           NA           NA           NA
## 4320          Yes           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1257           NA           NA           NA           NA           NA
## 4320           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1257           NA           NA           NA           NA           NA
## 4320           NA           NA           NA          Yes          Yes
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1257           NA           NA           NA           NA           NA
## 4320           NA           NA           NA          Yes           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 1257           No           NA           No Standard hours           NA
## 4320           NA           NA           NA             NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1257           NA           NA           NA           NA           NA
## 4320           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1257           NA           NA           NA           NA           NA
## 4320           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1257           NA           NA           NA           NA           NA
## 4320           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1257           NA           NA           NA           NA           NA
## 4320           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1257           NA           NA           NA           NA           NA
## 4320           NA           NA           No           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1257          Yes           NA           NA           NA           NA
## 4320           NA           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 1257           NA           NA           NA           NA           NA
## 4320           NA           NA           NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1257           NA           NA           NA           NA           NA
## 4320           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1257           NA           NA           NA           NA           NA
## 4320           NA           NA           NA           NA          Yes
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1257           NA           NA           NA           NA           NA
## 4320           NA           NA           NA          Yes          Yes
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1257           NA           NA           NA           NA           NA
## 4320           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1257         Rent     Optimist          Mom           No          Yes
## 4320         Rent     Optimist          Mom           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1257           NA          Yes          Yes      Check!          No
## 4320           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1257          No          NA          NA          NA          NA
## 4320          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 1257          NA          NA          NA
## 4320          NA          NA          NA
## [1] "Category: PKn"
## [1] "max distance(0.9796) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 3139    3912          R        PKn           NA          Yes           No
## 4323    5398          D        PKn           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 3139           No           Pc           No          Yes           No
## 4323           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 3139           No          Yes           No          Yes           No
## 4323           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 3139      Science          Yes  Study first           No           No
## 4323           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 3139           No       Giving           No          Yes           NA
## 4323           NA           NA           NA          Yes          Yes
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 3139           NA           NA           NA           NA           NA
## 4323           No           Id           No           NA   Hot headed
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 3139           NA           NA           NA           NA           NA
## 4323           No        Happy           No          Yes           No
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 3139           NA           NA          Yes        Start          Yes
## 4323           No           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 3139          Yes           Me          Yes           No          Yes
## 4323           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 3139           No          Yes          TMI          Yes          Yes
## 4323           NA           NA           NA           NA          Yes
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 3139           NA           NA           NA           NA           NA
## 4323        Tunes   Technology          Yes          Yes          Yes
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 3139           NA           NA           NA           NA           NA
## 4323           No          Yes   Supportive           No           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 3139           NA           NA           NA           NA           No
## 4323           NA           NA           NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 3139           NA           No    In-person           No          Yes
## 4323           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 3139          Yes          Yes           Gr          Yes          Yes
## 4323           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 3139          Yes           No           No          Yes           No
## 4323           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 3139           NA           NA           NA           NA           NA
## 4323           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 3139           NA           NA           NA           NA           NA
## 4323           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 3139           NA           NA           NA          NA          NA
## 4323           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 3139          NA          NA          NA          NA          NA
## 4323          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 3139          NA          NA          NA
## 4323          NA          NA          NA
## [1] "min distance(0.9690) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4738    5910          R        PKn           NA           NA           NA
## 6834    6356       <NA>        PKn           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4738           NA           NA           NA           NA           NA
## 6834           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4738           NA           NA           NA           NA           NA
## 6834           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4738           NA           NA           NA           NA           NA
## 6834           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4738           NA           NA           NA           NA           NA
## 6834           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 4738           NA           NA           NA           NA           NA
## 6834           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4738           NA           NA           NA           NA           NA
## 6834           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4738           NA           NA           NA           NA           NA
## 6834           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4738           NA           NA           NA           NA           NA
## 6834           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4738           No           No           NA           No           No
## 6834           No           No           NA           NA          Yes
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4738        Tunes       People           No           NA          Yes
## 6834        Tunes   Technology           No           NA          Yes
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4738           No          Yes           NA           NA           NA
## 6834           NA           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 4738           NA           NA           NA           NA           NA
## 6834           NA           NA           NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4738           NA           NA           NA           NA           NA
## 6834           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4738           NA           NA           NA           NA           NA
## 6834           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4738           NA           NA           NA           NA           NA
## 6834           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4738           NA           NA           NA           NA           NA
## 6834           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4738           NA           NA           NA           NA           NA
## 6834           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4738           NA           NA           NA          NA          NA
## 6834           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4738          NA          NA          NA          NA          NA
## 6834          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 4738          NA          NA         Yes
## 6834          NA          NA          NA
## [1] "No module detected"
## [1] "Category: PKy"
## [1] "max distance(0.9797) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1001    1244          R        PKy          Yes           NA          Yes
## 2346    2921          R        PKy           No           No          Yes
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1001           No           Pt           NA           NA           NA
## 2346           No           Pc           No           No          Yes
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1001           NA           NA           NA           NA           NA
## 2346           No          Yes          Yes          Yes          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1001          Art           NA  Study first           NA           NA
## 2346      Science          Yes  Study first          Yes           No
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1001           NA           NA           No           No           NA
## 2346          Yes       Giving          Yes          Yes           No
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 1001           NA           NA           NA           NA           NA
## 2346          Yes           Id           No    Odd hours  Cool headed
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1001           NA        Happy          Yes          Yes          Yes
## 2346           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1001           NA         A.M.          Yes          End          Yes
## 2346           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1001           NA           Me          Yes          Yes          Yes
## 2346           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1001          Yes           No   Mysterious           No           No
## 2346           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1001           NA           NA           NA           NA           NA
## 2346           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1001           NA           No   Supportive           No          Mac
## 2346           NA           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 1001           NA           NA           NA         Yes!           No
## 2346           NA           NA           NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1001    Socialize           No       Online           No           NA
## 2346           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1001          Yes           NA           NA           NA           NA
## 2346           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1001           NA           NA           NA           NA           NA
## 2346           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1001           NA          Yes           NA           NA           NA
## 2346           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1001           NA           NA           NA           NA           NA
## 2346           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1001           NA           NA           NA          NA          NA
## 2346           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1001          NA          NA          NA          NA          NA
## 2346          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 1001          NA          NA          NA
## 2346          NA          NA          NA
## [1] "min distance(0.9701) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2749    3419          R        PKy           NA           No          Yes
## 4636    5786          D        PKy           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2749           No           NA           NA           NA          Yes
## 4636           NA           Pt          Yes          Yes          Yes
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2749           No          Yes           No          Yes          Yes
## 4636           No          Yes           NA           No          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2749          Art           NA           NA           NA           NA
## 4636      Science           No  Study first           No           No
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2749           NA           NA           No           No          Yes
## 4636           No       Giving           No           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 2749           No           NA           No Standard hours  Cool headed
## 4636           NA           NA           NA             NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2749           No        Right          Yes           No          Yes
## 4636           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2749          Yes         P.M.           No        Start          Yes
## 4636           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2749           No           Me           No           No          Yes
## 4636           NA           NA          Yes           No           No
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2749           No           No          TMI          Yes           No
## 4636          Yes          Yes   Mysterious           No           No
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2749        Tunes   Technology           No          Yes          Yes
## 4636        Tunes   Technology           No          Yes          Yes
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2749           No           No    Demanding           No           NA
## 4636           No           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 2749          Yes           NA     Cautious       Umm...           No
## 4636           NA           NA           NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2749    Socialize           No       Online          Yes           No
## 4636           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2749          Yes           No           Gr           No           No
## 4636           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2749          Yes           No          Yes          Yes           No
## 4636           NA           NA           NA           NA           No
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2749          Yes          Yes           No           No           No
## 4636          Yes          Yes           No          Yes           No
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2749         Rent    Pessimist          Mom           No          Yes
## 4636         Rent     Optimist          Mom           No           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2749           No          Yes          Yes      Check!          No
## 4636           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2749         Yes         Yes          No          No          No
## 4636          NA          NA          No          No          No
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 2749         Yes          No          No
## 4636         Yes          No          No
## [1] "No module detected"
## [1] "Category: SKn"
## [1] "max distance(0.9809) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 5090    6355          R        SKn          Yes          Yes           No
## 6751    5960       <NA>        SKn           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 5090           No           Pc          Yes          Yes           No
## 6751           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 5090          Yes          Yes          Yes           No          Yes
## 6751           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 5090      Science           No  Study first           No           No
## 6751           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 5090          Yes       Giving          Yes          Yes          Yes
## 6751           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 5090          Yes           Id          Yes Standard hours  Cool headed
## 6751           NA           NA           NA             NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 5090          Yes        Happy          Yes          Yes           No
## 6751           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 5090          Yes         P.M.          Yes          End          Yes
## 6751           NA           NA           NA        Start           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 5090           No           Me           No          Yes          Yes
## 6751           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 5090          Yes           No   Mysterious           NA           NA
## 6751           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 5090           NA           NA           NA           NA           NA
## 6751           NA           NA           NA          Yes          Yes
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 5090           NA           NA           NA           NA           NA
## 6751           No           No   Supportive          Yes           PC
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 5090           NA           NA           NA           NA           NA
## 6751           NA           NA           NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 5090           NA           NA           NA           NA           NA
## 6751           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 5090           NA           NA           NA           NA           NA
## 6751           NA           NA           NA          Yes           No
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 5090           NA           NA           NA           NA           NA
## 6751          Yes          Yes           No          Yes          Yes
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 5090           NA           NA           NA           NA           NA
## 6751          Yes          Yes           No           No           No
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 5090           NA           NA           NA           NA           NA
## 6751         Rent           NA           NA           No           No
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 5090           NA           NA           NA          NA          NA
## 6751           No          Yes          Yes      Check!          No
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 5090          NA          NA          NA          NA          NA
## 6751          No          No          No         Yes          No
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 5090          NA          NA          NA
## 6751         Yes         Yes          NA
## [1] "min distance(0.9648) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 5901    1696       <NA>        SKn           NA           NA           NA
## 6327    3843       <NA>        SKn           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 5901           NA           NA           NA           NA           NA
## 6327           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 5901           NA           NA           NA           NA           NA
## 6327           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 5901           NA           NA           NA           NA           NA
## 6327           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 5901           NA           NA           NA           NA           NA
## 6327           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 5901           NA           NA           NA           NA           NA
## 6327           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 5901           NA           NA           NA           NA           NA
## 6327           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 5901           NA           NA           NA           NA           NA
## 6327           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 5901           NA           NA           NA           NA           NA
## 6327           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 5901           NA           NA           NA           NA           NA
## 6327           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 5901           NA           NA           NA           NA           NA
## 6327           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 5901           NA           NA    Demanding           NA           NA
## 6327           NA           NA           NA           NA          Mac
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 5901           NA           NA           NA           NA           No
## 6327           NA           NA           NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 5901    Socialize           NA           NA           NA          Yes
## 6327           NA           NA           NA           NA           No
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 5901           NA           NA           NA           NA           NA
## 6327           No           NA           Gr           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 5901           NA           NA           NA           NA           NA
## 6327           NA           NA           NA           NA          Yes
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 5901           NA           NA           NA           NA           NA
## 6327           NA           No           No           No           No
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 5901           NA           NA          Mom           NA           NA
## 6327           NA    Pessimist          Mom           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 5901           NA           NA           NA          NA          NA
## 6327           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 5901          NA          NA          NA          NA          NA
## 6327          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 5901  Only-child          NA          NA
## 6327          NA          NA          NA
## [1] "Category: SKy"
## [1] "max distance(0.9807) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 3522    4386          R        SKy           NA           NA           NA
## 6623    5332       <NA>        SKy           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 3522           NA           NA           NA           NA          Yes
## 6623           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 3522           No          Yes          Yes          Yes          Yes
## 6623           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 3522      Science          Yes  Study first           No           No
## 6623           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 3522           No       Giving           No          Yes          Yes
## 6623           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 3522          Yes           Pr           No Standard hours   Hot headed
## 6623           NA           NA           NA             NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 3522          Yes        Happy          Yes          Yes          Yes
## 6623           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 3522           No         P.M.           NA           NA           NA
## 6623           NA           NA          Yes        Start          Yes
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 3522           NA           NA           NA           NA           NA
## 6623           No           Me           No           NA           No
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 3522           NA           NA           NA           NA           NA
## 6623          Yes           No           NA          Yes           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 3522           NA           NA           NA           NA           NA
## 6623           NA           NA           NA          Yes          Yes
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 3522           NA           NA           NA           NA           NA
## 6623           No           No   Supportive          Yes          Mac
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 3522           NA           NA           NA           NA           NA
## 6623          Yes           NA     Cautious       Umm...          Yes
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 3522           NA           NA           NA           NA           NA
## 6623        Space          Yes           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 3522           NA           NA           NA           NA           NA
## 6623           NA           NA           NA          Yes           No
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 3522           NA           NA           NA           NA           NA
## 6623          Yes          Yes           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 3522           NA           NA           NA           NA           NA
## 6623           NA          Yes          Yes          Yes          Yes
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 3522         Rent           NA           NA           NA           NA
## 6623           NA     Optimist           NA          Yes           No
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 3522           NA           NA           NA          NA          NA
## 6623          Yes          Yes           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 3522         Yes         Yes          No          No          No
## 6623          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 3522         Yes          No          NA
## 6623          NA          NA          NA
## [1] "min distance(0.9678) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 3786    4714          D        SKy           NA          Yes          Yes
## 5803    1206       <NA>        SKy           No           No           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 3786           No           NA           NA           NA           NA
## 5803           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 3786           NA           NA           No          Yes           No
## 5803           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 3786          Art           NA           NA           NA           NA
## 5803           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 3786           NA           NA           NA           NA           NA
## 5803           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 3786           NA           NA           NA           NA           NA
## 5803           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 3786           NA           NA           NA           NA           NA
## 5803           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 3786           NA           NA           NA           NA           NA
## 5803           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 3786           NA           NA           NA           NA           NA
## 5803           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 3786           NA           NA           NA           NA           NA
## 5803           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 3786           NA           NA           NA           NA           NA
## 5803           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 3786           NA           NA           NA           NA           NA
## 5803           NA           NA   Supportive          Yes          Mac
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 3786           NA           NA           NA           NA           NA
## 5803          Yes           NA           NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 3786           NA           NA           NA           NA           NA
## 5803           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 3786           NA           NA           NA           NA           NA
## 5803           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 3786           NA           NA           NA           NA           NA
## 5803           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 3786           NA           No          Yes          Yes          Yes
## 5803           NA           No           No          Yes           No
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 3786         Rent     Optimist          Dad           No          Yes
## 5803          Own    Pessimist          Dad           No          Yes
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 3786          Yes           No           NA          NA          NA
## 5803           No          Yes           NA          NA          No
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 3786          NA          NA          NA          NA          NA
## 5803          No         Yes          No          No          No
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 3786         Yes          NA          No
## 5803         Yes          No         Yes
##    Hhold.fctr .clusterid Hhold.fctr.clusterid   D   R  .entropy .knt
## 1           N          1                  N_1  40  40 0.6931472   80
## 2           N          2                  N_2  30  29 0.6930035   59
## 3           N          3                  N_3  27  21 0.6853142   48
## 4           N          4                  N_4  13  25 0.6424220   38
## 5           N          5                  N_5  21  11 0.6434916   32
## 6         MKn          1                MKn_1  30  42 0.6791933   72
## 7         MKn          2                MKn_2  32  21 0.6714519   53
## 8         MKn          3                MKn_3  23  14 0.6632647   37
## 9         MKn          4                MKn_4  21  14 0.6730117   35
## 10        MKn          5                MKn_5  18  13 0.6800829   31
## 11        MKy          1                MKy_1 114 128 0.6914729  242
## 12        MKy          2                MKy_2  99  83 0.6892779  182
## 13        MKy          3                MKy_3  47  58 0.6876496  105
## 14        PKn          1                PKn_1  19   9 0.6279416   28
## 15        PKn          2                PKn_2   9   4 0.6172418   13
## 16        PKn          3                PKn_3   6   4 0.6730117   10
## 17        PKn          4                PKn_4   5   3 0.6615632    8
## 18        PKn          5                PKn_5   7   1 0.3767702    8
## 19        PKy          1                PKy_1   2   5 0.5982696    7
## 20        PKy          2                PKy_2   3   5 0.6615632    8
## 21        PKy          3                PKy_3   3   3 0.6931472    6
## 22        PKy          4                PKy_4   2   4 0.6365142    6
## 23        PKy          5                PKy_5   2   1 0.6365142    3
## 24        SKn          1                SKn_1 264 193 0.6810296  457
## 25        SKn          2                SKn_2  91  83 0.6920899  174
## 26        SKn          3                SKn_3  76  59 0.6851974  135
## 27        SKn          4                SKn_4  74  53 0.6794132  127
## 28        SKn          5                SKn_5  56  54 0.6929819  110
## 29        SKy          1                SKy_1  15  12 0.6869616   27
## 30        SKy          2                SKy_2  10  10 0.6931472   20
## 31        SKy          3                SKy_3   3   8 0.5859526   11
## 32        SKy          4                SKy_4   9   3 0.5623351   12
## [1] "glbObsAll$Hhold.fctr$.clusterid Entropy: 0.6800 (98.9950 pct)"
##                      label step_major step_minor label_minor     bgn
## 13            cluster.data          5          0           0 224.369
## 14 partition.data.training          6          0           0 281.029
##        end elapsed
## 13 281.028  56.659
## 14      NA      NA

Step 6.0: partition data training

## [1] "partition.data.training chunk: setup: elapsed: 0.00 secs"
## [1] "partition.data.training chunk: strata_mtrx complete: elapsed: 0.10 secs"
## [1] "partition.data.training chunk: obs_freq_df complete: elapsed: 0.10 secs"
## [1] "lclgetMatrixSimilarity: duration: 19.537000 secs"
## Loading required package: sampling
## 
## Attaching package: 'sampling'
## The following object is masked from 'package:caret':
## 
##     cluster
## Stratum 1 
## 
## Population total and number of selected units: 131 26 
## Stratum 2 
## 
## Population total and number of selected units: 124 21 
## Stratum 3 
## 
## Population total and number of selected units: 260 52 
## Stratum 4 
## 
## Population total and number of selected units: 46 6 
## Stratum 5 
## 
## Population total and number of selected units: 12 1 
## Stratum 6 
## 
## Population total and number of selected units: 561 119 
## Stratum 7 
## 
## Population total and number of selected units: 37 12 
## Stratum 8 
## 
## Population total and number of selected units: 126 22 
## Stratum 9 
## 
## Population total and number of selected units: 104 19 
## Stratum 10 
## 
## Population total and number of selected units: 269 45 
## Stratum 11 
## 
## Population total and number of selected units: 21 5 
## Stratum 12 
## 
## Population total and number of selected units: 18 1 
## Stratum 13 
## 
## Population total and number of selected units: 442 103 
## Stratum 14 
## 
## Population total and number of selected units: 33 11 
## Number of strata  14 
## Total number of selected units 443 
## [1] "lclgetMatrixSimilarity: duration: 11.991000 secs"
## [1] "lclgetMatrixSimilarity: duration: 4.123000 secs"
## [1] "lclgetMatrixSimilarity: duration: 3.900000 secs"
## [1] "lclgetMatrixSimilarity: duration: 8.880000 secs"

## [1] "Similarity of partitions:"
##         cor cosineSmy obs.x obs.y
## 1 0.9999862 0.8859246   OOB   Fit
## 2 0.9999863 0.9236707   OOB   New
## 3 0.9999864 0.8285690   Fit   New
## [1] "partition.data.training chunk: Fit/OOB partition complete: elapsed: 50.24 secs"
##     Party.Democrat Party.Republican Party.NA
##                 NA               NA      547
## Fit            934              807       NA
## OOB            237              206       NA
##     Party.Democrat Party.Republican Party.NA
##                 NA               NA        1
## Fit      0.5364733        0.4635267       NA
## OOB      0.5349887        0.4650113       NA
##   Hhold.fctr .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 6        SKn    781    222    276     0.44859276    0.501128668
## 2        MKy    432     97    121     0.24813326    0.218961625
## 3          N    209     48     59     0.12004595    0.108352144
## 1        MKn    188     40     49     0.10798392    0.090293454
## 7        SKy     47     23     28     0.02699598    0.051918736
## 4        PKn     56     11     12     0.03216542    0.024830700
## 5        PKy     28      2      2     0.01608271    0.004514673
##   .freqRatio.Tst
## 6    0.504570384
## 2    0.221206581
## 3    0.107861060
## 1    0.089579525
## 7    0.051188300
## 4    0.021937843
## 5    0.003656307
## [1] "glbObsAll: "
## [1] 2731  222
## [1] "glbObsTrn: "
## [1] 2184  222
## [1] "glbObsFit: "
## [1] 1741  221
## [1] "glbObsOOB: "
## [1] 443 221
## [1] "glbObsNew: "
## [1] 547 221
## [1] "partition.data.training chunk: teardown: elapsed: 50.87 secs"
##                      label step_major step_minor label_minor     bgn
## 14 partition.data.training          6          0           0 281.029
## 15         select.features          7          0           0 332.001
##        end elapsed
## 14 332.001  50.972
## 15      NA      NA

Step 7.0: select features

```{r select.features, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}

## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero
## [1] "cor(Q98059.fctr, Q98078.fctr)=0.7770"
## [1] "cor(Party.fctr, Q98059.fctr)=-0.0411"
## [1] "cor(Party.fctr, Q98078.fctr)=-0.0435"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q98059.fctr as highly correlated with Q98078.fctr
## [1] "cor(Q100562.fctr, Q100680.fctr)=0.7680"
## [1] "cor(Party.fctr, Q100562.fctr)=-0.0339"
## [1] "cor(Party.fctr, Q100680.fctr)=-0.0273"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q100680.fctr as highly correlated with Q100562.fctr
## [1] "cor(Q113583.fctr, Q113584.fctr)=0.7653"
## [1] "cor(Party.fctr, Q113583.fctr)=-0.0531"
## [1] "cor(Party.fctr, Q113584.fctr)=-0.0342"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q113584.fctr as highly correlated with Q113583.fctr
## [1] "cor(Q102674.fctr, Q102687.fctr)=0.7442"
## [1] "cor(Party.fctr, Q102674.fctr)=-0.0418"
## [1] "cor(Party.fctr, Q102687.fctr)=-0.0306"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q102687.fctr as highly correlated with Q102674.fctr
## [1] "cor(Q100562.fctr, Q100689.fctr)=0.7006"
## [1] "cor(Party.fctr, Q100562.fctr)=-0.0339"
## [1] "cor(Party.fctr, Q100689.fctr)=-0.0519"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q100562.fctr as highly correlated with Q100689.fctr
##                         cor.y exclude.as.feat    cor.y.abs   cor.high.X
## Gender.fctr      0.0909665772               0 0.0909665772         <NA>
## Q113181.fctr     0.0435755897               0 0.0435755897         <NA>
## .pos             0.0413144073               1 0.0413144073         <NA>
## USER_ID          0.0412669796               1 0.0412669796         <NA>
## Q120472.fctr     0.0373511418               0 0.0373511418         <NA>
## Q115611.fctr     0.0373054801               0 0.0373054801         <NA>
## Q120650.fctr     0.0358710311               0 0.0358710311         <NA>
## Q118237.fctr     0.0271389945               0 0.0271389945         <NA>
## .rnorm           0.0268489499               0 0.0268489499         <NA>
## Q122120.fctr     0.0234623979               0 0.0234623979         <NA>
## Q110740.fctr     0.0213122928               0 0.0213122928         <NA>
## Q122770.fctr     0.0203817957               0 0.0203817957         <NA>
## Q118117.fctr     0.0200889054               0 0.0200889054         <NA>
## Income.fctr      0.0178852251               0 0.0178852251         <NA>
## Q116441.fctr     0.0177162919               0 0.0177162919         <NA>
## Q118233.fctr     0.0176482250               0 0.0176482250         <NA>
## Q106272.fctr     0.0166660459               0 0.0166660459         <NA>
## Q119650.fctr     0.0154160890               0 0.0154160890         <NA>
## Q124742.fctr     0.0148384193               0 0.0148384193         <NA>
## Q122771.fctr     0.0146492330               0 0.0146492330         <NA>
## Q99480.fctr      0.0141594473               0 0.0141594473         <NA>
## Q116197.fctr     0.0130225570               0 0.0130225570         <NA>
## Q116881.fctr     0.0127923944               0 0.0127923944         <NA>
## Q101596.fctr     0.0122700322               0 0.0122700322         <NA>
## Q122769.fctr     0.0120730754               0 0.0120730754         <NA>
## Q108855.fctr     0.0116199609               0 0.0116199609         <NA>
## Q120014.fctr     0.0100200811               0 0.0100200811         <NA>
## Q119334.fctr     0.0097611771               0 0.0097611771         <NA>
## Q106993.fctr     0.0088906471               0 0.0088906471         <NA>
## Q107869.fctr     0.0084600631               0 0.0084600631         <NA>
## YOB              0.0065731919               1 0.0065731919         <NA>
## Q121011.fctr     0.0064795771               0 0.0064795771         <NA>
## Q117186.fctr     0.0061297032               0 0.0061297032         <NA>
## Q106997.fctr     0.0047472923               0 0.0047472923         <NA>
## YOB.Age.dff      0.0039888175               0 0.0039888175         <NA>
## Q108617.fctr     0.0034142713               0 0.0034142713         <NA>
## Q98197.fctr      0.0033385631               0 0.0033385631         <NA>
## Q106042.fctr     0.0028257871               0 0.0028257871         <NA>
## Q115777.fctr     0.0021874038               0 0.0021874038         <NA>
## Q123621.fctr     0.0020333068               0 0.0020333068         <NA>
## Q106388.fctr     0.0019532137               0 0.0019532137         <NA>
## Q114152.fctr    -0.0002141693               0 0.0002141693         <NA>
## Q124122.fctr    -0.0005523953               0 0.0005523953         <NA>
## Q120194.fctr    -0.0008725662               0 0.0008725662         <NA>
## Q116797.fctr    -0.0009782776               0 0.0009782776         <NA>
## Q105655.fctr    -0.0019537389               0 0.0019537389         <NA>
## Q115899.fctr    -0.0040294642               0 0.0040294642         <NA>
## Q116448.fctr    -0.0042193065               0 0.0042193065         <NA>
## Q117193.fctr    -0.0045436986               0 0.0045436986         <NA>
## Q108754.fctr    -0.0052510790               0 0.0052510790         <NA>
## Q108856.fctr    -0.0057486122               0 0.0057486122         <NA>
## YOB.Age.fctr    -0.0071871098               0 0.0071871098         <NA>
## Q123464.fctr    -0.0073497112               0 0.0073497112         <NA>
## Q99581.fctr     -0.0075725773               0 0.0075725773         <NA>
## Q114961.fctr    -0.0078051581               0 0.0078051581         <NA>
## Q104996.fctr    -0.0087935260               0 0.0087935260         <NA>
## Q108343.fctr    -0.0093294049               0 0.0093294049         <NA>
## Q120012.fctr    -0.0094832005               0 0.0094832005         <NA>
## Q120978.fctr    -0.0095190624               0 0.0095190624         <NA>
## Q98578.fctr     -0.0127194176               0 0.0127194176         <NA>
## Q103293.fctr    -0.0127467568               0 0.0127467568         <NA>
## Q106389.fctr    -0.0127995068               0 0.0127995068         <NA>
## Q98869.fctr     -0.0141131536               0 0.0141131536         <NA>
## Q112512.fctr    -0.0148254430               0 0.0148254430         <NA>
## Q116953.fctr    -0.0150205968               0 0.0150205968         <NA>
## Q100010.fctr    -0.0157954167               0 0.0157954167         <NA>
## Q111220.fctr    -0.0161563341               0 0.0161563341         <NA>
## Q102906.fctr    -0.0162667502               0 0.0162667502         <NA>
## Q121700.fctr    -0.0162998394               0 0.0162998394         <NA>
## Q112478.fctr    -0.0164349791               0 0.0164349791         <NA>
## .clusterid      -0.0164819548               1 0.0164819548         <NA>
## .clusterid.fctr -0.0164819548               0 0.0164819548         <NA>
## Q115610.fctr    -0.0179375585               0 0.0179375585         <NA>
## Q119851.fctr    -0.0188165770               0 0.0188165770         <NA>
## Q114517.fctr    -0.0194814883               0 0.0194814883         <NA>
## Q118892.fctr    -0.0197340603               0 0.0197340603         <NA>
## Q115602.fctr    -0.0202866077               0 0.0202866077         <NA>
## Q120379.fctr    -0.0203016988               0 0.0203016988         <NA>
## Q107491.fctr    -0.0205240116               0 0.0205240116         <NA>
## Q114748.fctr    -0.0209202111               0 0.0209202111         <NA>
## Q99982.fctr     -0.0215133899               0 0.0215133899         <NA>
## Q113992.fctr    -0.0222394292               0 0.0222394292         <NA>
## Q115390.fctr    -0.0224688906               0 0.0224688906         <NA>
## Q118232.fctr    -0.0257663213               0 0.0257663213         <NA>
## Q96024.fctr     -0.0265018957               0 0.0265018957         <NA>
## Q115195.fctr    -0.0271738479               0 0.0271738479         <NA>
## Q121699.fctr    -0.0273324911               0 0.0273324911         <NA>
## Q100680.fctr    -0.0273415528               0 0.0273415528 Q100562.fctr
## Q111580.fctr    -0.0274150724               0 0.0274150724         <NA>
## Q102289.fctr    -0.0285292574               0 0.0285292574         <NA>
## Q102687.fctr    -0.0306196219               0 0.0306196219 Q102674.fctr
## Q105840.fctr    -0.0307993280               0 0.0307993280         <NA>
## Q101162.fctr    -0.0310084074               0 0.0310084074         <NA>
## Q108950.fctr    -0.0317261524               0 0.0317261524         <NA>
## Q116601.fctr    -0.0325709549               0 0.0325709549         <NA>
## Q108342.fctr    -0.0332508344               0 0.0332508344         <NA>
## Q100562.fctr    -0.0338636276               0 0.0338636276 Q100689.fctr
## Q113584.fctr    -0.0341810079               0 0.0341810079 Q113583.fctr
## Q109367.fctr    -0.0343630284               0 0.0343630284         <NA>
## Q99716.fctr     -0.0374467543               0 0.0374467543         <NA>
## Hhold.fctr      -0.0382423557               0 0.0382423557         <NA>
## Q112270.fctr    -0.0396676511               0 0.0396676511         <NA>
## Q98059.fctr     -0.0411225217               0 0.0411225217  Q98078.fctr
## Q111848.fctr    -0.0412349958               0 0.0412349958         <NA>
## Q102674.fctr    -0.0417938234               0 0.0417938234         <NA>
## Q114386.fctr    -0.0423163811               0 0.0423163811         <NA>
## Q98078.fctr     -0.0434942851               0 0.0434942851         <NA>
## Q102089.fctr    -0.0480456671               0 0.0480456671         <NA>
## Edn.fctr        -0.0493632201               0 0.0493632201         <NA>
## Q100689.fctr    -0.0518568959               0 0.0518568959         <NA>
## Q113583.fctr    -0.0530628021               0 0.0530628021         <NA>
## Q101163.fctr    -0.0716366284               0 0.0716366284         <NA>
## Q109244.fctr               NA               0           NA         <NA>
##                 freqRatio percentUnique zeroVar   nzv is.cor.y.abs.low
## Gender.fctr      1.450753    0.13736264   FALSE FALSE            FALSE
## Q113181.fctr     3.968421    0.13736264   FALSE FALSE            FALSE
## .pos             1.000000  100.00000000   FALSE FALSE            FALSE
## USER_ID          1.000000  100.00000000   FALSE FALSE            FALSE
## Q120472.fctr     1.896389    0.13736264   FALSE FALSE            FALSE
## Q115611.fctr     2.705996    0.13736264   FALSE FALSE            FALSE
## Q120650.fctr     1.112016    0.13736264   FALSE FALSE            FALSE
## Q118237.fctr     3.284065    0.13736264   FALSE FALSE            FALSE
## .rnorm           1.000000  100.00000000   FALSE FALSE            FALSE
## Q122120.fctr     2.320883    0.13736264   FALSE FALSE             TRUE
## Q110740.fctr     4.361345    0.13736264   FALSE FALSE             TRUE
## Q122770.fctr     3.025974    0.13736264   FALSE FALSE             TRUE
## Q118117.fctr     2.587649    0.13736264   FALSE FALSE             TRUE
## Income.fctr      1.544160    0.32051282   FALSE FALSE             TRUE
## Q116441.fctr     3.578824    0.13736264   FALSE FALSE             TRUE
## Q118233.fctr     2.757576    0.13736264   FALSE FALSE             TRUE
## Q106272.fctr     4.321149    0.13736264   FALSE FALSE             TRUE
## Q119650.fctr     1.731915    0.13736264   FALSE FALSE             TRUE
## Q124742.fctr     8.808612    0.13736264   FALSE FALSE             TRUE
## Q122771.fctr     2.180534    0.13736264   FALSE FALSE             TRUE
## Q99480.fctr      3.603139    0.13736264   FALSE FALSE             TRUE
## Q116197.fctr     3.091858    0.13736264   FALSE FALSE             TRUE
## Q116881.fctr     3.634259    0.13736264   FALSE FALSE             TRUE
## Q101596.fctr     5.100304    0.13736264   FALSE FALSE             TRUE
## Q122769.fctr     3.500000    0.13736264   FALSE FALSE             TRUE
## Q108855.fctr    11.674847    0.13736264   FALSE FALSE             TRUE
## Q120014.fctr     2.410681    0.13736264   FALSE FALSE             TRUE
## Q119334.fctr     2.779193    0.13736264   FALSE FALSE             TRUE
## Q106993.fctr     3.783599    0.13736264   FALSE FALSE             TRUE
## Q107869.fctr     7.161826    0.13736264   FALSE FALSE             TRUE
## YOB              1.091743    3.43406593   FALSE FALSE             TRUE
## Q121011.fctr     2.049383    0.13736264   FALSE FALSE             TRUE
## Q117186.fctr     3.433409    0.13736264   FALSE FALSE             TRUE
## Q106997.fctr     6.438462    0.13736264   FALSE FALSE             TRUE
## YOB.Age.dff      1.098214    0.86996337   FALSE FALSE             TRUE
## Q108617.fctr     5.533742    0.13736264   FALSE FALSE             TRUE
## Q98197.fctr      5.296178    0.13736264   FALSE FALSE             TRUE
## Q106042.fctr     5.903915    0.13736264   FALSE FALSE             TRUE
## Q115777.fctr     4.316667    0.13736264   FALSE FALSE             TRUE
## Q123621.fctr     4.540059    0.13736264   FALSE FALSE             TRUE
## Q106388.fctr     4.481283    0.13736264   FALSE FALSE             TRUE
## Q114152.fctr     4.042929    0.13736264   FALSE FALSE             TRUE
## Q124122.fctr     4.057292    0.13736264   FALSE FALSE             TRUE
## Q120194.fctr     2.708163    0.13736264   FALSE FALSE             TRUE
## Q116797.fctr     4.049738    0.13736264   FALSE FALSE             TRUE
## Q105655.fctr     5.336634    0.13736264   FALSE FALSE             TRUE
## Q115899.fctr     4.610619    0.13736264   FALSE FALSE             TRUE
## Q116448.fctr     4.631420    0.13736264   FALSE FALSE             TRUE
## Q117193.fctr     3.419355    0.13736264   FALSE FALSE             TRUE
## Q108754.fctr     7.281746    0.13736264   FALSE FALSE             TRUE
## Q108856.fctr     8.883721    0.13736264   FALSE FALSE             TRUE
## YOB.Age.fctr     1.094828    0.41208791   FALSE FALSE             TRUE
## Q123464.fctr     2.323988    0.13736264   FALSE FALSE             TRUE
## Q99581.fctr      3.255578    0.13736264   FALSE FALSE             TRUE
## Q114961.fctr     4.412791    0.13736264   FALSE FALSE             TRUE
## Q104996.fctr     5.160256    0.13736264   FALSE FALSE             TRUE
## Q108343.fctr     7.047244    0.13736264   FALSE FALSE             TRUE
## Q120012.fctr     2.215613    0.13736264   FALSE FALSE             TRUE
## Q120978.fctr     1.991394    0.13736264   FALSE FALSE             TRUE
## Q98578.fctr      4.763689    0.13736264   FALSE FALSE             TRUE
## Q103293.fctr     5.266234    0.13736264   FALSE FALSE             TRUE
## Q106389.fctr     6.686275    0.13736264   FALSE FALSE             TRUE
## Q98869.fctr      4.231552    0.13736264   FALSE FALSE             TRUE
## Q112512.fctr     3.268994    0.13736264   FALSE FALSE             TRUE
## Q116953.fctr     3.706444    0.13736264   FALSE FALSE             TRUE
## Q100010.fctr     3.599558    0.13736264   FALSE FALSE             TRUE
## Q111220.fctr     3.793269    0.13736264   FALSE FALSE             TRUE
## Q102906.fctr     5.357827    0.13736264   FALSE FALSE             TRUE
## Q121700.fctr     1.584833    0.13736264   FALSE FALSE             TRUE
## Q112478.fctr     4.898507    0.13736264   FALSE FALSE             TRUE
## .clusterid       1.793713    0.22893773   FALSE FALSE             TRUE
## .clusterid.fctr  1.793713    0.22893773   FALSE FALSE             TRUE
## Q115610.fctr     2.595819    0.13736264   FALSE FALSE             TRUE
## Q119851.fctr     1.969543    0.13736264   FALSE FALSE             TRUE
## Q114517.fctr     3.109244    0.13736264   FALSE FALSE             TRUE
## Q118892.fctr     1.960591    0.13736264   FALSE FALSE             TRUE
## Q115602.fctr     2.656420    0.13736264   FALSE FALSE             TRUE
## Q120379.fctr     2.302326    0.13736264   FALSE FALSE             TRUE
## Q107491.fctr     3.898383    0.13736264   FALSE FALSE             TRUE
## Q114748.fctr     3.335664    0.13736264   FALSE FALSE             TRUE
## Q99982.fctr      5.702703    0.13736264   FALSE FALSE             TRUE
## Q113992.fctr     2.914172    0.13736264   FALSE FALSE             TRUE
## Q115390.fctr     4.036176    0.13736264   FALSE FALSE             TRUE
## Q118232.fctr     5.225490    0.13736264   FALSE FALSE             TRUE
## Q96024.fctr      5.104938    0.13736264   FALSE FALSE             TRUE
## Q115195.fctr     3.287912    0.13736264   FALSE FALSE            FALSE
## Q121699.fctr     1.704385    0.13736264   FALSE FALSE            FALSE
## Q100680.fctr     4.890533    0.13736264   FALSE FALSE            FALSE
## Q111580.fctr     4.118863    0.13736264   FALSE FALSE            FALSE
## Q102289.fctr     4.946429    0.13736264   FALSE FALSE            FALSE
## Q102687.fctr     5.698246    0.13736264   FALSE FALSE            FALSE
## Q105840.fctr     7.431034    0.13736264   FALSE FALSE            FALSE
## Q101162.fctr     5.211838    0.13736264   FALSE FALSE            FALSE
## Q108950.fctr     8.753488    0.13736264   FALSE FALSE            FALSE
## Q116601.fctr     2.490694    0.13736264   FALSE FALSE            FALSE
## Q108342.fctr     7.175299    0.13736264   FALSE FALSE            FALSE
## Q100562.fctr     4.191919    0.13736264   FALSE FALSE            FALSE
## Q113584.fctr     4.181058    0.13736264   FALSE FALSE            FALSE
## Q109367.fctr    11.134503    0.13736264   FALSE FALSE            FALSE
## Q99716.fctr      3.625821    0.13736264   FALSE FALSE            FALSE
## Hhold.fctr       1.896030    0.32051282   FALSE FALSE            FALSE
## Q112270.fctr     5.863799    0.13736264   FALSE FALSE            FALSE
## Q98059.fctr      2.971805    0.13736264   FALSE FALSE            FALSE
## Q111848.fctr     3.708229    0.13736264   FALSE FALSE            FALSE
## Q102674.fctr     4.988166    0.13736264   FALSE FALSE            FALSE
## Q114386.fctr     4.429395    0.13736264   FALSE FALSE            FALSE
## Q98078.fctr      6.255556    0.13736264   FALSE FALSE            FALSE
## Q102089.fctr     4.655271    0.13736264   FALSE FALSE            FALSE
## Edn.fctr         1.103359    0.36630037   FALSE FALSE            FALSE
## Q100689.fctr     4.553623    0.13736264   FALSE FALSE            FALSE
## Q113583.fctr     3.138655    0.13736264   FALSE FALSE            FALSE
## Q101163.fctr     6.202899    0.13736264   FALSE FALSE            FALSE
## Q109244.fctr     0.000000    0.04578755    TRUE  TRUE               NA
## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor
## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_point).

##              cor.y exclude.as.feat cor.y.abs cor.high.X freqRatio
## Q109244.fctr    NA               0        NA       <NA>         0
##              percentUnique zeroVar  nzv is.cor.y.abs.low
## Q109244.fctr    0.04578755    TRUE TRUE               NA
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.

## [1] "numeric data missing in : "
##        YOB Party.fctr 
##        239        547 
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff 
##         253 
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
##          Gender          Income HouseholdStatus  EducationLevel 
##              88             665             316             521 
##           Party         Q124742         Q124122         Q123464 
##              NA            2313            1954            1895 
##         Q123621         Q122769         Q122770         Q122771 
##            1928            1855            1758            1745 
##         Q122120         Q121699         Q121700         Q120978 
##            1719            1508            1543            1447 
##         Q121011         Q120379         Q120650         Q120472 
##            1447            1485            1367            1504 
##         Q120194         Q120012         Q120014         Q119334 
##            1657            1488            1641            1652 
##         Q119851         Q119650         Q118892         Q118117 
##            1458            1522            1493            1635 
##         Q118232         Q118233         Q118237         Q117186 
##            2006            1837            1789            1914 
##         Q117193         Q116797         Q116881         Q116953 
##            1868            1939            1978            1953 
##         Q116601         Q116441         Q116448         Q116197 
##            1847            1904            1927            1858 
##         Q115602         Q115777         Q115610         Q115611 
##            1844            1943            1870            1754 
##         Q115899         Q115390         Q114961         Q114748 
##            1956            1962            1905            1808 
##         Q115195         Q114517         Q114386         Q113992 
##            1880            1870            1951            1849 
##         Q114152         Q113583         Q113584         Q113181 
##            2031            1883            1901            1906 
##         Q112478         Q112512         Q112270         Q111848 
##            2067            2004            2050            1872 
##         Q111580         Q111220         Q110740         Q109367 
##            1993            1993            1949            2383 
##         Q108950         Q109244         Q108855         Q108617 
##            2346            2731            2379            2265 
##         Q108856         Q108754         Q108342         Q108343 
##            2388            2284            2262            2241 
##         Q107869         Q107491         Q106993         Q106997 
##            2177            2125            2100            2113 
##         Q106272         Q106388         Q106389         Q106042 
##            2084            2114            2140            2093 
##         Q105840         Q105655         Q104996         Q103293 
##            2167            2042            2019            2042 
##         Q102906         Q102674         Q102687         Q102289 
##            2116            2116            2038            2079 
##         Q102089         Q101162         Q101163         Q101596 
##            2052            2094            2152            2104 
##         Q100689         Q100680         Q100562          Q99982 
##            1969            2074            2080            2114 
##         Q100010          Q99716          Q99581          Q99480 
##            2033            2071            2010            2016 
##          Q98869          Q98578          Q98059          Q98078 
##            2088            2073            1984            2125 
##          Q98197          Q96024            .lcn 
##            2084            2065             547
## [1] "glb_feats_df:"
## [1] 113  12
##                    id exclude.as.feat rsp_var
## Party.fctr Party.fctr            TRUE    TRUE
##                    id      cor.y exclude.as.feat  cor.y.abs cor.high.X
## USER_ID       USER_ID 0.04126698            TRUE 0.04126698       <NA>
## Party.fctr Party.fctr         NA            TRUE         NA       <NA>
##            freqRatio percentUnique zeroVar   nzv is.cor.y.abs.low
## USER_ID            1           100   FALSE FALSE            FALSE
## Party.fctr        NA            NA      NA    NA               NA
##            interaction.feat shapiro.test.p.value rsp_var_raw id_var
## USER_ID                <NA>                   NA       FALSE   TRUE
## Party.fctr             <NA>                   NA          NA     NA
##            rsp_var
## USER_ID         NA
## Party.fctr    TRUE
## [1] "glb_feats_df vs. glbObsAll: "
## character(0)
## [1] "glbObsAll vs. glb_feats_df: "
## character(0)
##              label step_major step_minor label_minor     bgn     end
## 15 select.features          7          0           0 332.001 334.855
## 16      fit.models          8          0           0 334.855      NA
##    elapsed
## 15   2.854
## 16      NA

Step 8.0: fit models

fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_0_bgn          1          0       setup 335.572  NA      NA
# load(paste0(glbOut$pfx, "dsk.RData"))

glbgetModelSelectFormula <- function() {
    model_evl_terms <- c(NULL)
    # min.aic.fit might not be avl
    lclMdlEvlCriteria <- 
        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
    for (metric in lclMdlEvlCriteria)
        model_evl_terms <- c(model_evl_terms, 
                             ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
    if (glb_is_classification && glb_is_binomial)
        model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
    model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
    return(model_sel_frmla)
}

glbgetDisplayModelsDf <- function() {
    dsp_models_cols <- c("id", 
                    glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                    grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    dsp_models_df <- 
        #orderBy(glbgetModelSelectFormula(), glb_models_df)[, c("id", glbMdlMetricsEval)]
        orderBy(glbgetModelSelectFormula(), glb_models_df)[, dsp_models_cols]    
    nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
    nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0, 
        nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
    
#     nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
#     nParams <- nParams[names(nParams) != "avNNet"]    
    
    if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
        print("Cross Validation issues:")
        warning("Cross Validation issues:")        
        print(cvMdlProblems)
    }
    
    pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
    pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
    
    # length(pltMdls) == 21
    png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
    pltIx <- 1
    for (mdlId in pltMdls) {
        print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),   
              vp = viewport(layout.pos.row = ceiling(pltIx / 2.0), 
                            layout.pos.col = ((pltIx - 1) %% 2) + 1))  
        pltIx <- pltIx + 1
    }
    dev.off()

    if (all(row.names(dsp_models_df) != dsp_models_df$id))
        row.names(dsp_models_df) <- dsp_models_df$id
    return(dsp_models_df)
}
#glbgetDisplayModelsDf()

glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
    mdl <- glb_models_lst[[mdl_id]]
    
    clmnNames <- mygetPredictIds(rsp_var, mdl_id)
    predct_var_name <- clmnNames$value        
    predct_prob_var_name <- clmnNames$prob
    predct_accurate_var_name <- clmnNames$is.acc
    predct_error_var_name <- clmnNames$err
    predct_erabs_var_name <- clmnNames$err.abs

    if (glb_is_regression) {
        df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
                  facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
        if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="auto"))
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))
        
        df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }

    if (glb_is_classification && glb_is_binomial) {
        prob_threshold <- glb_models_df[glb_models_df$id == mdl_id, 
                                        "opt.prob.threshold.OOB"]
        if (is.null(prob_threshold) || is.na(prob_threshold)) {
            warning("Using default probability threshold: ", prob_threshold_def)
            if (is.null(prob_threshold <- prob_threshold_def))
                stop("Default probability threshold is NULL")
        }
        
        df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
        df[, predct_var_name] <- 
                factor(levels(df[, glb_rsp_var])[
                    (df[, predct_prob_var_name] >=
                        prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
    
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
#                   facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
#         if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="auto"))
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))
        
        # if prediction is a TP (true +ve), measure distance from 1.0
        tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
        #rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a TN (true -ve), measure distance from 0.0
        tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
        #rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FP (flse +ve), measure distance from 0.0
        fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
        #rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FN (flse -ve), measure distance from 1.0
        fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
        #rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]

        
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }    
    
    if (glb_is_classification && !glb_is_binomial) {
        df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
        probCls <- predict(mdl, newdata = df, type = "prob")        
        df[, predct_prob_var_name] <- NA
        for (cls in names(probCls)) {
            mask <- (df[, predct_var_name] == cls)
            df[mask, predct_prob_var_name] <- probCls[mask, cls]
        }    
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            fill_col_name = predct_var_name))
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            facet_frmla = paste0("~", glb_rsp_var)))
        
        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
        
        # if prediction is erroneous, measure predicted class prob from actual class prob
        df[, predct_erabs_var_name] <- 0
        for (cls in names(probCls)) {
            mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
            df[mask, predct_erabs_var_name] <- probCls[mask, cls]
        }    

        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])        
    }

    return(df)
}    

if (glb_is_classification && glb_is_binomial && 
        (length(unique(glbObsFit[, glb_rsp_var])) < 2))
    stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
         paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))

max_cor_y_x_vars <- orderBy(~ -cor.y.abs, 
        subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low & 
                                is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
    max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")

if (!is.null(glb_Baseline_mdl_var)) {
    if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) & 
        (glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] > 
         glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
        stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var, 
             " than the Baseline var: ", glb_Baseline_mdl_var)
}

glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
    
# Model specs
# c("id.prefix", "method", "type",
#   # trainControl params
#   "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
#   # train params
#   "metric", "metric.maximize", "tune.df")

# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                            paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
                                    label.minor = "mybaseln_classfr")
    ret_lst <- myfit_mdl(mdl_id="Baseline", 
                         model_method="mybaseln_classfr",
                        indepVar=glb_Baseline_mdl_var,
                        rsp_var=glb_rsp_var,
                        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    

# Most Frequent Outcome "MFO" model: mean(y) for regression
#   Not using caret's nullModel since model stats not avl
#   Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "MFO"), major.inc = FALSE,
                                        label.minor = "myMFO_classfr")

    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
        train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
                            indepVar = ".rnorm", rsp_var = glb_rsp_var,
                            fit_df = glbObsFit, OOB_df = glbObsOOB)

        # "random" model - only for classification; 
        #   none needed for regression since it is same as MFO
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "Random"), major.inc = FALSE,
                                        label.minor = "myrandom_classfr")

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)    
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
        train.method = "myrandom_classfr")),
                        indepVar = ".rnorm", rsp_var = glb_rsp_var,
                        fit_df = glbObsFit, OOB_df = glbObsOOB)
}
##              label step_major step_minor   label_minor     bgn     end
## 1 fit.models_0_bgn          1          0         setup 335.572 335.606
## 2 fit.models_0_MFO          1          1 myMFO_classfr 335.607      NA
##   elapsed
## 1   0.034
## 2      NA
## [1] "myfit_mdl: enter: 0.002000 secs"
## [1] "myfit_mdl: fitting model: MFO###myMFO_classfr"
## [1] "    indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.419000 secs"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] D R
## Levels: D R
## [1] "unique.prob:"
## y
##         D         R 
## 0.5364733 0.4635267 
## [1] "MFO.val:"
## [1] "D"
## [1] "myfit_mdl: train complete: 0.882000 secs"
##   parameter
## 1      none
##             Length Class      Mode     
## unique.vals 2      factor     numeric  
## unique.prob 2      -none-     numeric  
## MFO.val     1      -none-     character
## x.names     1      -none-     character
## xNames      1      -none-     character
## problemType 1      -none-     character
## tuneValue   1      data.frame list     
## obsLevels   2      -none-     character
## Warning in if (mdl_specs_lst[["train.method"]] == "glm")
## mydisplayOutliers(mdl, : the condition has length > 1 and only the first
## element will be used
## [1] "myfit_mdl: train diagnostics complete: 0.885000 secs"
## Loading required namespace: pROC
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## Loading required package: ROCR
## Loading required package: gplots
## 
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
## 
##     lowess
## [1] "in MFO.Classifier$prob"
##           D         R
## 1 0.5364733 0.4635267
## 2 0.5364733 0.4635267
## 3 0.5364733 0.4635267
## 4 0.5364733 0.4635267
## 5 0.5364733 0.4635267
## 6 0.5364733 0.4635267

##          Prediction
## Reference   D   R
##         D 934   0
##         R 807   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.364733e-01   0.000000e+00   5.127173e-01   5.601062e-01   5.364733e-01 
## AccuracyPValue  McnemarPValue 
##   5.098183e-01  4.412715e-177 
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
##           D         R
## 1 0.5364733 0.4635267
## 2 0.5364733 0.4635267
## 3 0.5364733 0.4635267
## 4 0.5364733 0.4635267
## 5 0.5364733 0.4635267
## 6 0.5364733 0.4635267
##          Prediction
## Reference   D   R
##         D 237   0
##         R 206   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.349887e-01   0.000000e+00   4.873124e-01   5.821948e-01   5.349887e-01 
## AccuracyPValue  McnemarPValue 
##   5.194323e-01   2.792063e-46 
## [1] "myfit_mdl: predict complete: 6.389000 secs"
##                    id  feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO###myMFO_classfr .rnorm               0                      0.455
##   min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1                 0.003             0.5            1            0
##   max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1             0.5                    0.5               0        0.5364733
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.5127173             0.5601062             0
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1             0.5            1            0             0.5
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                    0.5               0        0.5349887
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.4873124             0.5821948             0
## [1] "in MFO.Classifier$prob"
##           D         R
## 1 0.5364733 0.4635267
## 2 0.5364733 0.4635267
## 3 0.5364733 0.4635267
## 4 0.5364733 0.4635267
## 5 0.5364733 0.4635267
## 6 0.5364733 0.4635267
## [1] "myfit_mdl: exit: 6.436000 secs"
##                 label step_major step_minor      label_minor     bgn
## 2    fit.models_0_MFO          1          1    myMFO_classfr 335.607
## 3 fit.models_0_Random          1          2 myrandom_classfr 342.049
##       end elapsed
## 2 342.048   6.441
## 3      NA      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: Random###myrandom_classfr"
## [1] "    indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.417000 secs"
## Fitting parameter = none on full training set
## [1] "myfit_mdl: train complete: 0.723000 secs"
##   parameter
## 1      none
##             Length Class      Mode     
## unique.vals 2      factor     numeric  
## unique.prob 2      table      numeric  
## xNames      1      -none-     character
## problemType 1      -none-     character
## tuneValue   1      data.frame list     
## obsLevels   2      -none-     character
## Warning in if (mdl_specs_lst[["train.method"]] == "glm")
## mydisplayOutliers(mdl, : the condition has length > 1 and only the first
## element will be used

## [1] "myfit_mdl: train diagnostics complete: 0.725000 secs"
## [1] "in Random.Classifier$prob"

##          Prediction
## Reference   D   R
##         D 934   0
##         R 807   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.364733e-01   0.000000e+00   5.127173e-01   5.601062e-01   5.364733e-01 
## AccuracyPValue  McnemarPValue 
##   5.098183e-01  4.412715e-177 
## [1] "in Random.Classifier$prob"

##          Prediction
## Reference   D   R
##         D 237   0
##         R 206   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.349887e-01   0.000000e+00   4.873124e-01   5.821948e-01   5.349887e-01 
## AccuracyPValue  McnemarPValue 
##   5.194323e-01   2.792063e-46 
## [1] "myfit_mdl: predict complete: 6.838000 secs"
##                          id  feats max.nTuningRuns
## 1 Random###myrandom_classfr .rnorm               0
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      0.301                 0.002        0.483755
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.5214133    0.4460967       0.5049102                   0.55
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1               0        0.5364733             0.5127173
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.5601062             0       0.5555897    0.6160338
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.4951456       0.5054791                   0.55               0
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.5349887             0.4873124             0.5821948
##   max.Kappa.OOB
## 1             0
## [1] "in Random.Classifier$prob"
## [1] "myfit_mdl: exit: 7.571000 secs"
# Max.cor.Y
#   Check impact of cv
#       rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
                                    label.minor = "glmnet")
##                            label step_major step_minor      label_minor
## 3            fit.models_0_Random          1          2 myrandom_classfr
## 4 fit.models_0_Max.cor.Y.rcv.*X*          1          3           glmnet
##       bgn     end elapsed
## 3 342.049 349.633   7.584
## 4 349.634      NA      NA
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
    id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
    train.method = "glmnet")),
                    indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] "    indepVar: Gender.fctr,Q101163.fctr"
## [1] "myfit_mdl: setup complete: 0.685000 secs"
## Loading required package: glmnet
## Loading required package: Matrix
## 
## Attaching package: 'Matrix'
## The following object is masked from 'package:tidyr':
## 
##     expand
## Loaded glmnet 2.0-5
## Fitting alpha = 0.1, lambda = 0.00104 on full training set
## [1] "myfit_mdl: train complete: 1.488000 secs"
##   alpha     lambda
## 1   0.1 0.00104043

##             Length Class      Mode     
## a0           46    -none-     numeric  
## beta        184    dgCMatrix  S4       
## df           46    -none-     numeric  
## dim           2    -none-     numeric  
## lambda       46    -none-     numeric  
## dev.ratio    46    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## classnames    2    -none-     character
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames        4    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     2    -none-     character
## [1] "min lambda > lambdaOpt:"
##     (Intercept)    Gender.fctrF    Gender.fctrM Q101163.fctrDad 
##     -0.29183047     -0.02826816      0.34103527      0.16410896 
## Q101163.fctrMom 
##     -0.67736154 
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)"     "Gender.fctrF"    "Gender.fctrM"    "Q101163.fctrDad"
## [5] "Q101163.fctrMom"
## [1] "myfit_mdl: train diagnostics complete: 1.597000 secs"

##          Prediction
## Reference   D   R
##         D 495 439
##         R 329 478
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.588742e-01   1.211729e-01   5.351811e-01   5.823686e-01   5.364733e-01 
## AccuracyPValue  McnemarPValue 
##   3.201504e-02   8.382289e-05

##          Prediction
## Reference   D   R
##         D 218  19
##         R 181  25
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.485327e-01   4.342381e-02   5.008705e-01   5.955427e-01   5.349887e-01 
## AccuracyPValue  McnemarPValue 
##   3.004764e-01   5.000028e-30 
## [1] "myfit_mdl: predict complete: 7.259000 secs"
##                           id                    feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1###glmnet Gender.fctr,Q101163.fctr               0
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      0.796                 0.027       0.5611479
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.5299786    0.5923172       0.5742002                    0.5
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.5545244        0.5588742             0.5351811
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.5823686     0.1211729       0.5471714    0.5021097
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1     0.592233       0.5505203                   0.55             0.2
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.5485327             0.5008705             0.5955427
##   max.Kappa.OOB
## 1    0.04342381
## [1] "myfit_mdl: exit: 7.325000 secs"
if (glbMdlCheckRcv) {
    # rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
    for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
        for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
            
            # Experiment specific code to avoid caret crash
    #         lcl_tune_models_df <- rbind(data.frame()
    #                             ,data.frame(method = "glmnet", parameter = "alpha", 
    #                                         vals = "0.100 0.325 0.550 0.775 1.000")
    #                             ,data.frame(method = "glmnet", parameter = "lambda",
    #                                         vals = "9.342e-02")    
    #                                     )
            
            ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
                list(
                id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats), 
                type = glb_model_type, 
    # tune.df = lcl_tune_models_df,            
                trainControl.method = "repeatedcv",
                trainControl.number = rcv_n_folds, 
                trainControl.repeats = rcv_n_repeats,
                trainControl.classProbs = glb_is_classification,
                trainControl.summaryFunction = glbMdlMetricSummaryFn,
                train.method = "glmnet", train.metric = glbMdlMetricSummary, 
                train.maximize = glbMdlMetricMaximize)),
                                indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                                fit_df = glbObsFit, OOB_df = glbObsOOB)
        }
    # Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
    tmp_models_cols <- c("id", "max.nTuningRuns",
                        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                        grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    print(myplot_parcoord(obs_df = subset(glb_models_df, 
                                          grepl("Max.cor.Y.rcv.", id, fixed = TRUE), 
                                            select = -feats)[, tmp_models_cols],
                          id_var = "id"))
}
        
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
#                     paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
#                                     label.minor = "rpart")
# 
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
#     id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
#     train.method = "rpart",
#     tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
#                     indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var, 
#                     fit_df=glbObsFit, OOB_df=glbObsOOB)

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                        id.prefix = "Max.cor.Y", 
                        type = glb_model_type, trainControl.method = "repeatedcv",
                        trainControl.number = glb_rcv_n_folds, 
                        trainControl.repeats = glb_rcv_n_repeats,
                        trainControl.classProbs = glb_is_classification,
                        trainControl.summaryFunction = glbMdlMetricSummaryFn,
                        trainControl.allowParallel = glbMdlAllowParallel,                        
                        train.metric = glbMdlMetricSummary, 
                        train.maximize = glbMdlMetricMaximize,    
                        train.method = "rpart")),
                    indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Max.cor.Y##rcv#rpart"
## [1] "    indepVar: Gender.fctr,Q101163.fctr"
## [1] "myfit_mdl: setup complete: 0.693000 secs"
## Loading required package: rpart
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.00604 on full training set
## [1] "myfit_mdl: train complete: 2.102000 secs"
## Loading required package: rpart.plot

## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7, 
##     cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, 
##     surrogatestyle = 0, maxdepth = 30, xval = 0))
##   n= 1741 
## 
##           CP nsplit rel error
## 1 0.02416357      0 1.0000000
## 2 0.00000000      2 0.9516729
## 
## Variable importance
## Q101163.fctrMom    Gender.fctrM    Gender.fctrF 
##              40              31              29 
## 
## Node number 1: 1741 observations,    complexity param=0.02416357
##   predicted class=D  expected loss=0.4635267  P(node) =1
##     class counts:   934   807
##    probabilities: 0.536 0.464 
##   left son=2 (160 obs) right son=3 (1581 obs)
##   Primary splits:
##       Q101163.fctrMom < 0.5 to the right, improve=9.423110, (0 missing)
##       Gender.fctrM    < 0.5 to the left,  improve=8.554592, (0 missing)
##       Gender.fctrF    < 0.5 to the right, improve=7.376294, (0 missing)
##       Q101163.fctrDad < 0.5 to the left,  improve=1.465965, (0 missing)
## 
## Node number 2: 160 observations
##   predicted class=D  expected loss=0.3  P(node) =0.09190121
##     class counts:   112    48
##    probabilities: 0.700 0.300 
## 
## Node number 3: 1581 observations,    complexity param=0.02416357
##   predicted class=D  expected loss=0.4800759  P(node) =0.9080988
##     class counts:   822   759
##    probabilities: 0.520 0.480 
##   left son=6 (664 obs) right son=7 (917 obs)
##   Primary splits:
##       Gender.fctrM    < 0.5 to the left,  improve=7.408454, (0 missing)
##       Gender.fctrF    < 0.5 to the right, improve=6.635045, (0 missing)
##       Q101163.fctrDad < 0.5 to the left,  improve=0.734389, (0 missing)
##   Surrogate splits:
##       Gender.fctrF < 0.5 to the right, agree=0.968, adj=0.923, (0 split)
## 
## Node number 6: 664 observations
##   predicted class=D  expected loss=0.4231928  P(node) =0.38139
##     class counts:   383   281
##    probabilities: 0.577 0.423 
## 
## Node number 7: 917 observations
##   predicted class=R  expected loss=0.478735  P(node) =0.5267088
##     class counts:   439   478
##    probabilities: 0.479 0.521 
## 
## n= 1741 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 1741 807 D (0.5364733 0.4635267)  
##   2) Q101163.fctrMom>=0.5 160  48 D (0.7000000 0.3000000) *
##   3) Q101163.fctrMom< 0.5 1581 759 D (0.5199241 0.4800759)  
##     6) Gender.fctrM< 0.5 664 281 D (0.5768072 0.4231928) *
##     7) Gender.fctrM>=0.5 917 439 R (0.4787350 0.5212650) *
## [1] "myfit_mdl: train diagnostics complete: 2.964000 secs"

## [1] "mypredict_mdl: maxMetricDf:"
##    threshold   f.score  accuracy
## 10      0.45 0.5545244 0.5588742
## 11      0.50 0.5545244 0.5588742

##          Prediction
## Reference   D   R
##         D 495 439
##         R 329 478
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.588742e-01   1.211729e-01   5.351811e-01   5.823686e-01   5.364733e-01 
## AccuracyPValue  McnemarPValue 
##   3.201504e-02   8.382289e-05

## [1] "mypredict_mdl: maxMetricDf:"
##    threshold   f.score  accuracy
## 10      0.45 0.5470852 0.5440181
## 11      0.50 0.5470852 0.5440181

##          Prediction
## Reference   D   R
##         D 119 118
##         R  84 122
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##     0.54401806     0.09333522     0.49634736     0.59109720     0.53498871 
## AccuracyPValue  McnemarPValue 
##     0.36981158     0.02023983 
## [1] "myfit_mdl: predict complete: 8.837000 secs"
##                     id                    feats max.nTuningRuns
## 1 Max.cor.Y##rcv#rpart Gender.fctr,Q101163.fctr               5
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      1.403                 0.012       0.5611479
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.5299786    0.5923172         0.56983                    0.5
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.5545244         0.558868             0.5351811
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.5823686     0.1211457       0.5471714    0.5021097
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1     0.592233       0.5511143                    0.5       0.5470852
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.5440181             0.4963474             0.5910972
##   max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1    0.09333522         0.01891925      0.03781364
## [1] "myfit_mdl: exit: 8.914000 secs"
if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Max.cor.Y.Time.Poly", 
            type = glb_model_type, trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,            
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Time.Lag", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,        
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if (length(glbFeatsText) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.nonTP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,                                
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyT", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,        
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA), 
                                subset(glb_feats_df, nzv)$id)) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
                                    label.minor = "glmnet")

    ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
        id.prefix="Interact.High.cor.Y", 
        type=glb_model_type, trainControl.method="repeatedcv",
        trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method="glmnet")),
        indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
        rsp_var=glb_rsp_var, 
        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    
##                              label step_major step_minor label_minor
## 4   fit.models_0_Max.cor.Y.rcv.*X*          1          3      glmnet
## 5 fit.models_0_Interact.High.cor.Y          1          4      glmnet
##       bgn     end elapsed
## 4 349.634 365.915  16.281
## 5 365.916      NA      NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Interact.High.cor.Y##rcv#glmnet"
## [1] "    indepVar: Gender.fctr,Q101163.fctr,Gender.fctr:Q100562.fctr,Gender.fctr:Q102674.fctr,Gender.fctr:Q100689.fctr,Gender.fctr:Q113583.fctr,Gender.fctr:Q98078.fctr"
## [1] "myfit_mdl: setup complete: 0.725000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.00483 on full training set
## [1] "myfit_mdl: train complete: 3.433000 secs"

##             Length Class      Mode     
## a0            64   -none-     numeric  
## beta        2176   dgCMatrix  S4       
## df            64   -none-     numeric  
## dim            2   -none-     numeric  
## lambda        64   -none-     numeric  
## dev.ratio     64   -none-     numeric  
## nulldev        1   -none-     numeric  
## npasses        1   -none-     numeric  
## jerr           1   -none-     numeric  
## offset         1   -none-     logical  
## classnames     2   -none-     character
## call           5   -none-     call     
## nobs           1   -none-     numeric  
## lambdaOpt      1   -none-     numeric  
## xNames        34   -none-     character
## problemType    1   -none-     character
## tuneValue      2   data.frame list     
## obsLevels      2   -none-     character
## [1] "min lambda > lambdaOpt:"
##                    (Intercept)                   Gender.fctrM 
##                   -0.296116702                    0.384875316 
##                Q101163.fctrDad                Q101163.fctrMom 
##                    0.239136757                   -0.573952050 
##    Gender.fctrF:Q100562.fctrNo    Gender.fctrM:Q100562.fctrNo 
##                    0.757657100                    0.405282922 
##   Gender.fctrN:Q100562.fctrYes   Gender.fctrF:Q100562.fctrYes 
##                   -1.388511415                    0.369754473 
##   Gender.fctrM:Q100562.fctrYes    Gender.fctrN:Q100689.fctrNo 
##                   -0.185322539                   -1.497720041 
##    Gender.fctrF:Q100689.fctrNo   Gender.fctrN:Q100689.fctrYes 
##                   -0.223231972                    1.729952318 
##   Gender.fctrF:Q100689.fctrYes   Gender.fctrM:Q100689.fctrYes 
##                   -0.223549442                   -0.224211153 
##    Gender.fctrN:Q102674.fctrNo    Gender.fctrF:Q102674.fctrNo 
##                    0.669008565                   -0.023769150 
##    Gender.fctrM:Q102674.fctrNo   Gender.fctrF:Q102674.fctrYes 
##                    0.221925496                   -0.731761825 
##   Gender.fctrM:Q102674.fctrYes  Gender.fctrN:Q113583.fctrTalk 
##                   -0.007445843                    2.753045773 
##  Gender.fctrF:Q113583.fctrTalk  Gender.fctrM:Q113583.fctrTalk 
##                   -0.180685129                    0.177823281 
## Gender.fctrN:Q113583.fctrTunes Gender.fctrF:Q113583.fctrTunes 
##                    0.384692060                   -0.097491545 
## Gender.fctrM:Q113583.fctrTunes     Gender.fctrN:Q98078.fctrNo 
##                   -0.267589962                   -1.170188735 
##     Gender.fctrF:Q98078.fctrNo     Gender.fctrM:Q98078.fctrNo 
##                   -0.034329453                   -0.172824227 
##    Gender.fctrN:Q98078.fctrYes    Gender.fctrF:Q98078.fctrYes 
##                    0.202015017                   -0.127696392 
##    Gender.fctrM:Q98078.fctrYes 
##                   -0.099161250 
## [1] "max lambda < lambdaOpt:"
##                    (Intercept)                   Gender.fctrM 
##                   -0.296198099                    0.386598546 
##                Q101163.fctrDad                Q101163.fctrMom 
##                    0.243672991                   -0.573977658 
##    Gender.fctrF:Q100562.fctrNo    Gender.fctrM:Q100562.fctrNo 
##                    0.784779498                    0.409295833 
##   Gender.fctrN:Q100562.fctrYes   Gender.fctrF:Q100562.fctrYes 
##                   -1.511831394                    0.393405406 
##   Gender.fctrM:Q100562.fctrYes    Gender.fctrN:Q100689.fctrNo 
##                   -0.186732556                   -1.566905404 
##    Gender.fctrF:Q100689.fctrNo    Gender.fctrM:Q100689.fctrNo 
##                   -0.242312964                   -0.001548176 
##   Gender.fctrN:Q100689.fctrYes   Gender.fctrF:Q100689.fctrYes 
##                    1.857025887                   -0.240616162 
##   Gender.fctrM:Q100689.fctrYes    Gender.fctrN:Q102674.fctrNo 
##                   -0.229526327                    0.719021643 
##    Gender.fctrF:Q102674.fctrNo    Gender.fctrM:Q102674.fctrNo 
##                   -0.028539659                    0.226248904 
##   Gender.fctrF:Q102674.fctrYes   Gender.fctrM:Q102674.fctrYes 
##                   -0.739945024                   -0.007752804 
##  Gender.fctrN:Q113583.fctrTalk  Gender.fctrF:Q113583.fctrTalk 
##                    2.849206894                   -0.185642064 
##  Gender.fctrM:Q113583.fctrTalk Gender.fctrN:Q113583.fctrTunes 
##                    0.179544299                    0.382807827 
## Gender.fctrF:Q113583.fctrTunes Gender.fctrM:Q113583.fctrTunes 
##                   -0.099021903                   -0.270480111 
##     Gender.fctrN:Q98078.fctrNo     Gender.fctrF:Q98078.fctrNo 
##                   -1.201651743                   -0.039508747 
##     Gender.fctrM:Q98078.fctrNo    Gender.fctrN:Q98078.fctrYes 
##                   -0.177336610                    0.218714021 
##    Gender.fctrF:Q98078.fctrYes    Gender.fctrM:Q98078.fctrYes 
##                   -0.132997741                   -0.103779257 
## [1] "myfit_mdl: train diagnostics complete: 4.112000 secs"

##          Prediction
## Reference   D   R
##         D 588 346
##         R 387 420
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   0.5789775991   0.1505163190   0.5553817730   0.6023067691   0.5364732912 
## AccuracyPValue  McnemarPValue 
##   0.0001991788   0.1395594155

##          Prediction
## Reference   D   R
##         D 152  85
##         R 104 102
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##     0.57336343     0.13732420     0.52581675     0.61992398     0.53498871 
## AccuracyPValue  McnemarPValue 
##     0.05776524     0.19043026 
## [1] "myfit_mdl: predict complete: 10.146000 secs"
##                                id
## 1 Interact.High.cor.Y##rcv#glmnet
##                                                                                                                                                  feats
## 1 Gender.fctr,Q101163.fctr,Gender.fctr:Q100562.fctr,Gender.fctr:Q102674.fctr,Gender.fctr:Q100689.fctr,Gender.fctr:Q113583.fctr,Gender.fctr:Q98078.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                      2.698                 0.114
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.5749982    0.6295503    0.5204461       0.6082637
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.5       0.5340114        0.5540889
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.5553818             0.6023068     0.1004791
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.5682479    0.6413502    0.4951456       0.5847057
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                    0.5        0.519084        0.5733634
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.5258168              0.619924     0.1373242
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01751427      0.03444344
## [1] "myfit_mdl: exit: 10.237000 secs"
# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
                                     label.minor = "glmnet")
##                              label step_major step_minor label_minor
## 5 fit.models_0_Interact.High.cor.Y          1          4      glmnet
## 6           fit.models_0_Low.cor.X          1          5      glmnet
##       bgn    end elapsed
## 5 365.916 376.17  10.254
## 6 376.170     NA      NA
indepVar <- mygetIndepVar(glb_feats_df)
indepVar <- setdiff(indepVar, unique(glb_feats_df$cor.high.X))
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Low.cor.X", 
            type = glb_model_type, 
            tune.df = glbMdlTuneParams,        
            trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indepVar = indepVar, rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Low.cor.X##rcv#glmnet"
## [1] "    indepVar: Gender.fctr,Q113181.fctr,Q120472.fctr,Q115611.fctr,Q120650.fctr,Q118237.fctr,.rnorm,Q122120.fctr,Q110740.fctr,Q122770.fctr,Q118117.fctr,Income.fctr,Q116441.fctr,Q118233.fctr,Q106272.fctr,Q119650.fctr,Q124742.fctr,Q122771.fctr,Q99480.fctr,Q116197.fctr,Q116881.fctr,Q101596.fctr,Q122769.fctr,Q108855.fctr,Q120014.fctr,Q119334.fctr,Q106993.fctr,Q107869.fctr,Q121011.fctr,Q117186.fctr,Q106997.fctr,Q108617.fctr,Q98197.fctr,Q106042.fctr,Q115777.fctr,Q123621.fctr,Q106388.fctr,Q114152.fctr,Q124122.fctr,Q120194.fctr,Q116797.fctr,Q105655.fctr,Q115899.fctr,Q116448.fctr,Q117193.fctr,Q108754.fctr,Q108856.fctr,YOB.Age.fctr,Q123464.fctr,Q99581.fctr,Q114961.fctr,Q104996.fctr,Q108343.fctr,Q120012.fctr,Q120978.fctr,Q98578.fctr,Q103293.fctr,Q106389.fctr,Q98869.fctr,Q112512.fctr,Q116953.fctr,Q100010.fctr,Q111220.fctr,Q102906.fctr,Q121700.fctr,Q112478.fctr,Q115610.fctr,Q119851.fctr,Q114517.fctr,Q118892.fctr,Q115602.fctr,Q120379.fctr,Q107491.fctr,Q114748.fctr,Q99982.fctr,Q113992.fctr,Q115390.fctr,Q118232.fctr,Q96024.fctr,Q115195.fctr,Q121699.fctr,Q100680.fctr,Q111580.fctr,Q102289.fctr,Q102687.fctr,Q105840.fctr,Q101162.fctr,Q108950.fctr,Q116601.fctr,Q108342.fctr,Q113584.fctr,Q109367.fctr,Q99716.fctr,Hhold.fctr,Q112270.fctr,Q98059.fctr,Q111848.fctr,Q114386.fctr,Q102089.fctr,Edn.fctr,Q101163.fctr,YOB.Age.fctr:YOB.Age.dff,Hhold.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.690000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.025 on full training set
## [1] "myfit_mdl: train complete: 8.849000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0             66  -none-     numeric  
## beta        16962  dgCMatrix  S4       
## df             66  -none-     numeric  
## dim             2  -none-     numeric  
## lambda         66  -none-     numeric  
## dev.ratio      66  -none-     numeric  
## nulldev         1  -none-     numeric  
## npasses         1  -none-     numeric  
## jerr            1  -none-     numeric  
## offset          1  -none-     logical  
## classnames      2  -none-     character
## call            5  -none-     call     
## nobs            1  -none-     numeric  
## lambdaOpt       1  -none-     numeric  
## xNames        257  -none-     character
## problemType     1  -none-     character
## tuneValue       2  data.frame list     
## obsLevels       2  -none-     character
## [1] "min lambda > lambdaOpt:"
##                  (Intercept)                   Edn.fctr.L 
##                  -0.24856701                  -0.01825592 
##                   Edn.fctr^4                 Gender.fctrM 
##                   0.07665110                   0.18955032 
##                Hhold.fctrPKn              Q101163.fctrMom 
##                  -0.12291763                  -0.36793581 
##               Q106389.fctrNo    Q108950.fctrRisk-friendly 
##                   0.06086792                  -0.09022225 
##              Q109367.fctrYes              Q111848.fctrYes 
##                  -0.05743248                  -0.07642739 
##              Q113181.fctrYes              Q114386.fctrTMI 
##                   0.40453966                  -0.09999585 
##               Q115611.fctrNo              Q115611.fctrYes 
##                  -0.16296329                   0.33579493 
##               Q116441.fctrNo              Q116441.fctrYes 
##                  -0.06532856                   0.08177310 
##               Q116601.fctrNo               Q119851.fctrNo 
##                   0.03737031                   0.11382449 
##               Q120379.fctrNo              Q120379.fctrYes 
##                   0.05079844                  -0.01759372 
##               Q120650.fctrNo                Q98197.fctrNo 
##                  -0.11841144                  -0.17031844 
##                Q98869.fctrNo               YOB.Age.fctr^8 
##                  -0.08319400                   0.05473263 
## Hhold.fctrN:.clusterid.fctr4 
##                   0.13636242 
## [1] "max lambda < lambdaOpt:"
##                    (Intercept)                     Edn.fctr.L 
##                   -0.254809720                   -0.030292261 
##                     Edn.fctr^4                     Edn.fctr^7 
##                    0.090562354                   -0.009556109 
##                   Gender.fctrM                  Hhold.fctrPKn 
##                    0.201605249                   -0.151465263 
##                  Hhold.fctrPKy                Q101163.fctrMom 
##                    0.051095847                   -0.391738029 
##                 Q106389.fctrNo      Q108950.fctrRisk-friendly 
##                    0.097899763                   -0.127445461 
##                Q109367.fctrYes                Q111848.fctrYes 
##                   -0.088886066                   -0.093960249 
##                Q113181.fctrYes                Q114386.fctrTMI 
##                    0.431607566                   -0.122732213 
##                 Q115611.fctrNo                Q115611.fctrYes 
##                   -0.163786252                    0.356695958 
##                 Q115899.fctrCs                 Q116441.fctrNo 
##                   -0.014762123                   -0.080866559 
##                Q116441.fctrYes                 Q116601.fctrNo 
##                    0.095518086                    0.072326701 
##                 Q119851.fctrNo                 Q120012.fctrNo 
##                    0.130530450                    0.011016916 
##                 Q120379.fctrNo                Q120379.fctrYes 
##                    0.048212945                   -0.038918039 
##            Q120472.fctrScience                 Q120650.fctrNo 
##                    0.007834446                   -0.151433426 
##                 Q122771.fctrPt                  Q98197.fctrNo 
##                    0.019838964                   -0.185300085 
##                  Q98869.fctrNo                 Q99480.fctrYes 
##                   -0.106502355                    0.010079056 
##                 YOB.Age.fctr^8   Hhold.fctrN:.clusterid.fctr4 
##                    0.082089959                    0.184580610 
## Hhold.fctrSKy:.clusterid.fctr4 
##                   -0.010609690 
## [1] "myfit_mdl: train diagnostics complete: 9.559000 secs"

##          Prediction
## Reference   D   R
##         D 495 439
##         R 239 568
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.105686e-01   2.299455e-01   5.872056e-01   6.335582e-01   5.364733e-01 
## AccuracyPValue  McnemarPValue 
##   2.665901e-10   2.129630e-14

##          Prediction
## Reference   D   R
##         D 111 126
##         R  60 146
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.801354e-01   1.734577e-01   5.326406e-01   6.265530e-01   5.349887e-01 
## AccuracyPValue  McnemarPValue 
##   3.136438e-02   1.878901e-06 
## [1] "myfit_mdl: predict complete: 18.739000 secs"
##                      id
## 1 Low.cor.X##rcv#glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           feats
## 1 Gender.fctr,Q113181.fctr,Q120472.fctr,Q115611.fctr,Q120650.fctr,Q118237.fctr,.rnorm,Q122120.fctr,Q110740.fctr,Q122770.fctr,Q118117.fctr,Income.fctr,Q116441.fctr,Q118233.fctr,Q106272.fctr,Q119650.fctr,Q124742.fctr,Q122771.fctr,Q99480.fctr,Q116197.fctr,Q116881.fctr,Q101596.fctr,Q122769.fctr,Q108855.fctr,Q120014.fctr,Q119334.fctr,Q106993.fctr,Q107869.fctr,Q121011.fctr,Q117186.fctr,Q106997.fctr,Q108617.fctr,Q98197.fctr,Q106042.fctr,Q115777.fctr,Q123621.fctr,Q106388.fctr,Q114152.fctr,Q124122.fctr,Q120194.fctr,Q116797.fctr,Q105655.fctr,Q115899.fctr,Q116448.fctr,Q117193.fctr,Q108754.fctr,Q108856.fctr,YOB.Age.fctr,Q123464.fctr,Q99581.fctr,Q114961.fctr,Q104996.fctr,Q108343.fctr,Q120012.fctr,Q120978.fctr,Q98578.fctr,Q103293.fctr,Q106389.fctr,Q98869.fctr,Q112512.fctr,Q116953.fctr,Q100010.fctr,Q111220.fctr,Q102906.fctr,Q121700.fctr,Q112478.fctr,Q115610.fctr,Q119851.fctr,Q114517.fctr,Q118892.fctr,Q115602.fctr,Q120379.fctr,Q107491.fctr,Q114748.fctr,Q99982.fctr,Q113992.fctr,Q115390.fctr,Q118232.fctr,Q96024.fctr,Q115195.fctr,Q121699.fctr,Q100680.fctr,Q111580.fctr,Q102289.fctr,Q102687.fctr,Q105840.fctr,Q101162.fctr,Q108950.fctr,Q116601.fctr,Q108342.fctr,Q113584.fctr,Q109367.fctr,Q99716.fctr,Hhold.fctr,Q112270.fctr,Q98059.fctr,Q111848.fctr,Q114386.fctr,Q102089.fctr,Edn.fctr,Q101163.fctr,YOB.Age.fctr:YOB.Age.dff,Hhold.fctr:.clusterid.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                      8.077                 0.631
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.5904459    0.8190578     0.361834       0.6668517
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                   0.45       0.6262404        0.5764843
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.5872056             0.6335582     0.1267896
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.5447954    0.7594937    0.3300971        0.606325
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                   0.45       0.6108787        0.5801354
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.5326406              0.626553     0.1734577
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01749145      0.03668815
## [1] "myfit_mdl: exit: 19.022000 secs"
fit.models_0_chunk_df <- 
    myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
                label.minor = "teardown")
##                    label step_major step_minor label_minor     bgn     end
## 6 fit.models_0_Low.cor.X          1          5      glmnet 376.170 395.233
## 7       fit.models_0_end          1          6    teardown 395.234      NA
##   elapsed
## 6  19.063
## 7      NA
rm(ret_lst)

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)
##         label step_major step_minor label_minor     bgn     end elapsed
## 16 fit.models          8          0           0 334.855 395.249  60.395
## 17 fit.models          8          1           1 395.250      NA      NA

```{r fit.models_1, cache=FALSE, fig.height=10, fig.width=15, eval=myevlChunk(glbChunks, glbOut$pfx)}

##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_1_bgn          1          0       setup 400.289  NA      NA
##                label step_major step_minor label_minor     bgn     end
## 1   fit.models_1_bgn          1          0       setup 400.289 400.302
## 2 fit.models_1_All.X          1          1       setup 400.303      NA
##   elapsed
## 1   0.013
## 2      NA
##                label step_major step_minor label_minor     bgn     end
## 2 fit.models_1_All.X          1          1       setup 400.303 400.311
## 3 fit.models_1_All.X          1          2      glmnet 400.311      NA
##   elapsed
## 2   0.008
## 3      NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: All.X##rcv#glmnet"
## [1] "    indepVar: Gender.fctr,Q113181.fctr,Q120472.fctr,Q115611.fctr,Q120650.fctr,Q118237.fctr,.rnorm,Q122120.fctr,Q110740.fctr,Q122770.fctr,Q118117.fctr,Income.fctr,Q116441.fctr,Q118233.fctr,Q106272.fctr,Q119650.fctr,Q124742.fctr,Q122771.fctr,Q99480.fctr,Q116197.fctr,Q116881.fctr,Q101596.fctr,Q122769.fctr,Q108855.fctr,Q120014.fctr,Q119334.fctr,Q106993.fctr,Q107869.fctr,Q121011.fctr,Q117186.fctr,Q106997.fctr,Q108617.fctr,Q98197.fctr,Q106042.fctr,Q115777.fctr,Q123621.fctr,Q106388.fctr,Q114152.fctr,Q124122.fctr,Q120194.fctr,Q116797.fctr,Q105655.fctr,Q115899.fctr,Q116448.fctr,Q117193.fctr,Q108754.fctr,Q108856.fctr,YOB.Age.fctr,Q123464.fctr,Q99581.fctr,Q114961.fctr,Q104996.fctr,Q108343.fctr,Q120012.fctr,Q120978.fctr,Q98578.fctr,Q103293.fctr,Q106389.fctr,Q98869.fctr,Q112512.fctr,Q116953.fctr,Q100010.fctr,Q111220.fctr,Q102906.fctr,Q121700.fctr,Q112478.fctr,Q115610.fctr,Q119851.fctr,Q114517.fctr,Q118892.fctr,Q115602.fctr,Q120379.fctr,Q107491.fctr,Q114748.fctr,Q99982.fctr,Q113992.fctr,Q115390.fctr,Q118232.fctr,Q96024.fctr,Q115195.fctr,Q121699.fctr,Q100680.fctr,Q111580.fctr,Q102289.fctr,Q102687.fctr,Q105840.fctr,Q101162.fctr,Q108950.fctr,Q116601.fctr,Q108342.fctr,Q100562.fctr,Q113584.fctr,Q109367.fctr,Q99716.fctr,Hhold.fctr,Q112270.fctr,Q98059.fctr,Q111848.fctr,Q102674.fctr,Q114386.fctr,Q98078.fctr,Q102089.fctr,Edn.fctr,Q100689.fctr,Q113583.fctr,Q101163.fctr,YOB.Age.fctr:YOB.Age.dff,Hhold.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.715000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.025 on full training set
## [1] "myfit_mdl: train complete: 10.150000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0             66  -none-     numeric  
## beta        17622  dgCMatrix  S4       
## df             66  -none-     numeric  
## dim             2  -none-     numeric  
## lambda         66  -none-     numeric  
## dev.ratio      66  -none-     numeric  
## nulldev         1  -none-     numeric  
## npasses         1  -none-     numeric  
## jerr            1  -none-     numeric  
## offset          1  -none-     logical  
## classnames      2  -none-     character
## call            5  -none-     call     
## nobs            1  -none-     numeric  
## lambdaOpt       1  -none-     numeric  
## xNames        267  -none-     character
## problemType     1  -none-     character
## tuneValue       2  data.frame list     
## obsLevels       2  -none-     character
## [1] "min lambda > lambdaOpt:"
##                  (Intercept)                   Edn.fctr.L 
##                  -0.24633071                  -0.01740549 
##                   Edn.fctr^4                 Gender.fctrM 
##                   0.07520599                   0.18732069 
##                Hhold.fctrPKn               Q100562.fctrNo 
##                  -0.12110652                   0.04313257 
##              Q101163.fctrMom              Q102674.fctrYes 
##                  -0.36238096                  -0.08862783 
##               Q106389.fctrNo    Q108950.fctrRisk-friendly 
##                   0.06215632                  -0.08896436 
##              Q109367.fctrYes              Q111848.fctrYes 
##                  -0.05341932                  -0.06772586 
##              Q113181.fctrYes            Q113583.fctrTunes 
##                   0.40792456                  -0.02284092 
##              Q114386.fctrTMI               Q115611.fctrNo 
##                  -0.09555340                  -0.16037163 
##              Q115611.fctrYes               Q116441.fctrNo 
##                   0.33873395                  -0.06557350 
##              Q116441.fctrYes               Q116601.fctrNo 
##                   0.09057368                   0.03483364 
##               Q119851.fctrNo               Q120379.fctrNo 
##                   0.11360341                   0.05302166 
##              Q120379.fctrYes               Q120650.fctrNo 
##                  -0.01506287                  -0.11989437 
##                Q98197.fctrNo                Q98869.fctrNo 
##                  -0.16546422                  -0.08143157 
##               YOB.Age.fctr^8 Hhold.fctrN:.clusterid.fctr4 
##                   0.05402258                   0.13343316 
## [1] "max lambda < lambdaOpt:"
##                  (Intercept)                   Edn.fctr.L 
##                 -0.251661553                 -0.029652441 
##                   Edn.fctr^4                   Edn.fctr^7 
##                  0.088012057                 -0.010038296 
##                 Gender.fctrM                Hhold.fctrPKn 
##                  0.198708881                 -0.148849865 
##                Hhold.fctrPKy               Q100562.fctrNo 
##                  0.048154800                  0.074016756 
##              Q101163.fctrMom              Q101596.fctrYes 
##                 -0.387635203                  0.002693313 
##              Q102674.fctrYes               Q106389.fctrNo 
##                 -0.119440274                  0.096390006 
##    Q108950.fctrRisk-friendly              Q109367.fctrYes 
##                 -0.125504143                 -0.083765949 
##              Q111848.fctrYes              Q113181.fctrYes 
##                 -0.081825884                  0.435899680 
##            Q113583.fctrTunes              Q114386.fctrTMI 
##                 -0.037510699                 -0.116421093 
##               Q115611.fctrNo              Q115611.fctrYes 
##                 -0.159119674                  0.359777700 
##               Q115899.fctrCs               Q116441.fctrNo 
##                 -0.016176456                 -0.081489290 
##              Q116441.fctrYes               Q116601.fctrNo 
##                  0.106122378                  0.070002273 
##               Q119851.fctrNo               Q120012.fctrNo 
##                  0.130494940                  0.010313790 
##               Q120379.fctrNo              Q120379.fctrYes 
##                  0.052638428                 -0.033521781 
##          Q120472.fctrScience               Q120650.fctrNo 
##                  0.003987252                 -0.152898742 
##               Q122771.fctrPt                Q98197.fctrNo 
##                  0.018750434                 -0.181192288 
##                Q98869.fctrNo               Q99480.fctrYes 
##                 -0.106090726                  0.017688107 
##               YOB.Age.fctr^8 Hhold.fctrN:.clusterid.fctr4 
##                  0.081116701                  0.180407402 
## [1] "myfit_mdl: train diagnostics complete: 10.828000 secs"

##          Prediction
## Reference   D   R
##         D 773 161
##         R 507 300
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.163125e-01   2.053563e-01   5.930025e-01   6.392296e-01   5.364733e-01 
## AccuracyPValue  McnemarPValue 
##   1.069346e-11   1.209380e-40

##          Prediction
## Reference   D   R
##         D 115 122
##         R  56 150
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.981941e-01   2.090079e-01   5.508809e-01   6.441871e-01   5.349887e-01 
## AccuracyPValue  McnemarPValue 
##   4.280853e-03   1.104988e-06 
## [1] "myfit_mdl: predict complete: 20.270000 secs"
##                  id
## 1 All.X##rcv#glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           feats
## 1 Gender.fctr,Q113181.fctr,Q120472.fctr,Q115611.fctr,Q120650.fctr,Q118237.fctr,.rnorm,Q122120.fctr,Q110740.fctr,Q122770.fctr,Q118117.fctr,Income.fctr,Q116441.fctr,Q118233.fctr,Q106272.fctr,Q119650.fctr,Q124742.fctr,Q122771.fctr,Q99480.fctr,Q116197.fctr,Q116881.fctr,Q101596.fctr,Q122769.fctr,Q108855.fctr,Q120014.fctr,Q119334.fctr,Q106993.fctr,Q107869.fctr,Q121011.fctr,Q117186.fctr,Q106997.fctr,Q108617.fctr,Q98197.fctr,Q106042.fctr,Q115777.fctr,Q123621.fctr,Q106388.fctr,Q114152.fctr,Q124122.fctr,Q120194.fctr,Q116797.fctr,Q105655.fctr,Q115899.fctr,Q116448.fctr,Q117193.fctr,Q108754.fctr,Q108856.fctr,YOB.Age.fctr,Q123464.fctr,Q99581.fctr,Q114961.fctr,Q104996.fctr,Q108343.fctr,Q120012.fctr,Q120978.fctr,Q98578.fctr,Q103293.fctr,Q106389.fctr,Q98869.fctr,Q112512.fctr,Q116953.fctr,Q100010.fctr,Q111220.fctr,Q102906.fctr,Q121700.fctr,Q112478.fctr,Q115610.fctr,Q119851.fctr,Q114517.fctr,Q118892.fctr,Q115602.fctr,Q120379.fctr,Q107491.fctr,Q114748.fctr,Q99982.fctr,Q113992.fctr,Q115390.fctr,Q118232.fctr,Q96024.fctr,Q115195.fctr,Q121699.fctr,Q100680.fctr,Q111580.fctr,Q102289.fctr,Q102687.fctr,Q105840.fctr,Q101162.fctr,Q108950.fctr,Q116601.fctr,Q108342.fctr,Q100562.fctr,Q113584.fctr,Q109367.fctr,Q99716.fctr,Hhold.fctr,Q112270.fctr,Q98059.fctr,Q111848.fctr,Q102674.fctr,Q114386.fctr,Q98078.fctr,Q102089.fctr,Edn.fctr,Q100689.fctr,Q113583.fctr,Q101163.fctr,YOB.Age.fctr:YOB.Age.dff,Hhold.fctr:.clusterid.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                      9.352                  0.75
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.5996852    0.8276231    0.3717472       0.6711596
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.5       0.4731861        0.5764853
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.5930025             0.6392296     0.1273592
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1        0.551442    0.7679325    0.3349515       0.6177236
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                   0.45       0.6276151        0.5981941
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.5508809             0.6441871     0.2090079
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01604823      0.03327071
## [1] "myfit_mdl: exit: 20.555000 secs"
##                  label step_major step_minor label_minor     bgn     end
## 3   fit.models_1_All.X          1          2      glmnet 400.311 420.892
## 4 fit.models_1_preProc          1          3     preProc 420.892      NA
##   elapsed
## 3  20.581
## 4      NA
##                                 min.elapsedtime.everything
## Random###myrandom_classfr                            0.301
## MFO###myMFO_classfr                                  0.455
## Max.cor.Y.rcv.1X1###glmnet                           0.796
## Max.cor.Y##rcv#rpart                                 1.403
## Interact.High.cor.Y##rcv#glmnet                      2.698
## Low.cor.X##rcv#glmnet                                8.077
## All.X##rcv#glmnet                                    9.352
##                  label step_major step_minor label_minor     bgn     end
## 4 fit.models_1_preProc          1          3     preProc 420.892 420.939
## 5     fit.models_1_end          1          4    teardown 420.940      NA
##   elapsed
## 4   0.047
## 5      NA
##         label step_major step_minor label_minor    bgn     end elapsed
## 17 fit.models          8          1           1 395.25 420.949  25.699
## 18 fit.models          8          2           2 420.95      NA      NA

```{r fit.models_2, cache=FALSE, fig.height=10, fig.width=15, eval=myevlChunk(glbChunks, glbOut$pfx)}

# if (sum(is.na(glbObsAll$D.P.http)) > 0)
#         stop("fit.models_3: Why is this happening ?")

#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
    # Merge or cbind ?
    for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
        glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
    for (col in setdiff(names(glbObsFit), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
    if (all(is.na(glbObsNew[, glb_rsp_var])))
        for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
            glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
    for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
    
print(setdiff(names(glbObsNew), names(glbObsAll)))

replay.petrisim(pn = glb_analytics_pn, 
    replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "model.selected")), flip_coord = TRUE)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)

Step 8.2: fit models

```{r fit.data.training_0, cache=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}

#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial) 
    prob_threshold <- glb_models_df[glb_models_df$id == glbMdlSelId,
                                        "opt.prob.threshold.OOB"] else 
    prob_threshold <- NULL

if (grepl("Ensemble", glbMdlFinId)) {
    # Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
    mdlEnsembleComps <- unlist(str_split(subset(glb_models_df, 
                                                id == glbMdlFinId)$feats, ","))
    if (glb_is_classification)
    #     mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
    # mdlEnsembleComps <- gsub(paste0("^", 
    #                     gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
    #                          "", mdlEnsembleComps)
        mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
                        mygetPredictIds(glb_rsp_var, thsMdlId)$prob  %in% mdlEnsembleComps)] else
        mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
                        mygetPredictIds(glb_rsp_var, thsMdlId)$value  %in% mdlEnsembleComps)]
                        
    for (mdl_id in mdlEnsembleComps) {
        glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
        glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
        # glb_fin_mdl uses the same coefficients as glb_sel_mdl, 
        #   so copy the "Final" columns into "non-Final" columns
        glbObsTrn[, gsub("Final.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
            glbObsTrn[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
        glbObsNew[, gsub("Final.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
            glbObsNew[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
    }    
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId, 
                                     rsp_var = glb_rsp_var,
                                    prob_threshold_def = prob_threshold)

glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
                                          featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glbMdlFinId, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)
if (glb_is_classification && glb_is_binomial)
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId, 
            prob_threshold=glb_models_df[glb_models_df$id == glbMdlSelId, 
                                         "opt.prob.threshold.OOB"]) else
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId)                  

dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
    dsp_feats_vctr <- union(dsp_feats_vctr, 
                            glb_feats_df[!is.na(glb_feats_df[, var]), "id"])

# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids, 
#                     grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])

print(setdiff(names(glbObsTrn), names(glbObsAll)))
for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]

print(setdiff(names(glbObsFit), names(glbObsAll)))
print(setdiff(names(glbObsOOB), names(glbObsAll)))
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
    
print(setdiff(names(glbObsNew), names(glbObsAll)))

#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]); 

replay.petrisim(pn = glb_analytics_pn, 
    replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "data.training.all.prediction","model.final")), flip_coord = TRUE)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)

Step 8.2: fit models

Null Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.

##                        label step_major step_minor label_minor     bgn
## 2               inspect.data          2          0           0  19.975
## 16                fit.models          8          0           0 334.855
## 13              cluster.data          5          0           0 224.369
## 14   partition.data.training          6          0           0 281.029
## 3                 scrub.data          2          1           1 176.309
## 17                fit.models          8          1           1 395.250
## 1                import.data          1          0           0   7.843
## 15           select.features          7          0           0 332.001
## 11      extract.features.end          3          6           6 222.765
## 12       manage.missing.data          4          0           0 223.706
## 10   extract.features.string          3          5           5 222.695
## 9      extract.features.text          3          4           4 222.633
## 7     extract.features.image          3          2           2 222.539
## 4             transform.data          2          2           2 222.430
## 6  extract.features.datetime          3          1           1 222.496
## 8     extract.features.price          3          3           3 222.595
## 5           extract.features          3          0           0 222.474
##        end elapsed duration
## 2  176.308 156.333  156.333
## 16 395.249  60.395   60.394
## 13 281.028  56.659   56.659
## 14 332.001  50.972   50.972
## 3  222.429  46.121   46.120
## 17 420.949  25.699   25.699
## 1   19.975  12.132   12.132
## 15 334.855   2.854    2.854
## 11 223.705   0.940    0.940
## 12 224.368   0.662    0.662
## 10 222.765   0.070    0.070
## 9  222.695   0.062    0.062
## 7  222.595   0.056    0.056
## 4  222.473   0.044    0.043
## 6  222.538   0.042    0.042
## 8  222.632   0.037    0.037
## 5  222.495   0.021    0.021
## [1] "Total Elapsed Time: 420.949 secs"